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    {
     "cell_type": "code",
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     "input": [
      "from __future__ import print_function \n",
      "import seaborn as sns\n",
      "from IPython.core.display import HTML\n",
      "from IPython.display import Image\n",
      "import pandas as pd\n",
      "import calendar\n",
      "\n",
      "from nilmtk.disaggregate.co_1d import CO_1d\n",
      "from nilmtk.cross_validation import train_test_split\n",
      "import nilmtk.preprocessing.electricity.building as prepb\n",
      "import nilmtk.stats.electricity.building as bstats\n",
      "import nilmtk.stats.electricity.single as sstats\n",
      "from nilmtk.dataset import DataSet\n",
      "from nilmtk.dataset import REDD\n",
      "from nilmtk.plots import plot_series\n",
      "from nilmtk.sensors.electricity import Measurement\n",
      "from nilmtk.metrics import (mean_normalized_error_power, \n",
      "                            fraction_energy_assigned_correctly, \n",
      "                            f_score,\n",
      "                            rms_error_power)\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings(\"ignore\")\n",
      "\n",
      "sns.set(font=\"serif\")\n",
      "\n",
      "def pretty_print_dict(dictionary):\n",
      "    html = '<ul>'\n",
      "    for key, value in dictionary.iteritems():\n",
      "        html += '<li><strong>{}</strong>: '.format(key)\n",
      "        if isinstance(value, list):\n",
      "            html += '<ul>'\n",
      "            for item in value:\n",
      "                html += '<li>{}</li>'.format(item)\n",
      "            html += '</ul></li>'\n",
      "        else:\n",
      "            html += '{}</li>'.format(value)\n",
      "    html += '</ul>'\n",
      "    display(HTML(html))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Demo of NILMTK v0.1 for NILM Workshop 2014 "
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Loading Data"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Load raw REDD .dat data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from nilmtk.dataset import REDD\n",
      "\n",
      "redd = REDD()\n",
      "redd.load('/data/REDD/low_freq/')\n",
      "\n",
      "# Note increase in system memory usage.\n",
      "# NILMTK v0.2 does not eagerly load data like v0.1 does."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "Loading building 1:\n",
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Examine dataset metadata"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pretty_print_dict(redd.metadata)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<ul><li><strong>name</strong>: REDD</li><li><strong>full_name</strong>: Reference Energy Disaggregation Data Set</li><li><strong>citations</strong>: <ul><li>J. Zico Kolter and Matthew J. Johnson. REDD: A public data set for energy disaggregation research. In proceedings of the SustKDD workshop on Data Mining Applications in Sustainability, 2011.</li></ul></li><li><strong>urls</strong>: <ul><li>http://redd.csail.mit.edu</li></ul></li><li><strong>timezone</strong>: US/Eastern</li><li><strong>geographic_coordinates</strong>: (42.360091, -71.09416)</li></ul>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x7f21b32c7590>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Appliance names are converted to use the NILMTK controlled vocabulary"
     ]
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "Raw REDD appliance names:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!cat '/data/REDD/low_freq/house_1/labels.dat'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1 mains\r\n",
        "2 mains\r\n",
        "3 oven\r\n",
        "4 oven\r\n",
        "5 refrigerator\r\n",
        "6 dishwaser\r\n",
        "7 kitchen_outlets\r\n",
        "8 kitchen_outlets\r\n",
        "9 lighting\r\n",
        "10 washer_dryer\r\n",
        "11 microwave\r\n",
        "12 bathroom_gfi\r\n",
        "13 electric_heat\r\n",
        "14 stove\r\n",
        "15 kitchen_outlets\r\n",
        "16 kitchen_outlets\r\n",
        "17 lighting\r\n",
        "18 lighting\r\n",
        "19 washer_dryer\r\n",
        "20 washer_dryer\r\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "NILMTK appliance names"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "electric = redd.buildings[1].utility.electric\n",
      "electric.appliances.keys()\n",
      "\n",
      "# Note that the washer dryer has been converted to a single DualSupply appliance"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "[ApplianceName(name='fridge', instance=1),\n",
        " ApplianceName(name='washer dryer', instance=1),\n",
        " ApplianceName(name='kitchen outlets', instance=2),\n",
        " ApplianceName(name='lighting', instance=3),\n",
        " ApplianceName(name='kitchen outlets', instance=1),\n",
        " ApplianceName(name='space heater', instance=1),\n",
        " ApplianceName(name='kitchen outlets', instance=4),\n",
        " ApplianceName(name='kitchen outlets', instance=3),\n",
        " ApplianceName(name='lighting', instance=1),\n",
        " ApplianceName(name='dishwasher', instance=1),\n",
        " ApplianceName(name='microwave', instance=1),\n",
        " ApplianceName(name='bathroom misc', instance=1),\n",
        " ApplianceName(name='lighting', instance=2),\n",
        " ApplianceName(name='oven', instance=1),\n",
        " ApplianceName(name='hob', instance=1)]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Get data for a particular appliance"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fridge = electric.appliances['fridge', 1]\n",
      "\n",
      "# fridge is a 2-column matrix (a Python Pandas DataFrame object)\n",
      "\n",
      "fridge.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>(power, active)</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2011-04-18 09:22:13-04:00</th>\n",
        "      <td> 6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-04-18 09:22:16-04:00</th>\n",
        "      <td> 6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-04-18 09:22:20-04:00</th>\n",
        "      <td> 6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-04-18 09:22:23-04:00</th>\n",
        "      <td> 6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-04-18 09:22:26-04:00</th>\n",
        "      <td> 6</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 1 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "                           (power, active)\n",
        "2011-04-18 09:22:13-04:00                6\n",
        "2011-04-18 09:22:16-04:00                6\n",
        "2011-04-18 09:22:20-04:00                6\n",
        "2011-04-18 09:22:23-04:00                6\n",
        "2011-04-18 09:22:26-04:00                6\n",
        "\n",
        "[5 rows x 1 columns]"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fridge[\"2011-04-18\":\"2011-04-19\"].plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f2194c37690>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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WedvmGysaeejZtzn9MwcwYeyIotNd/IdnAJiy6xh2mziq6Pj3P/4WDz+3kuVr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\neQcR8Dfv1XWehQu0yUbTQqP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       "text": [
        "<matplotlib.figure.Figure at 0x7f2194c372d0>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### HDF5 file format\n",
      "\n",
      "#### Export to HDF5\n",
      "\n",
      "```python\n",
      "DATA_DIRECTORY = '/path/to/hdf5/'\n",
      "redd.export(DATA_DIRECTORY)\n",
      "```\n",
      "\n",
      "#### Load from HDF5\n",
      "\n",
      "Note that we use the generic `DataSet` class to load HDF5...\n",
      "\n",
      "```python\n",
      "from nilmtk.dataset import DataSet\n",
      "dataset = DataSet()\n",
      "dataset.load_hdf5(DATA_DIRECTORY)\n",
      "```"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Dataset Diagnostics and Statistics"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![](static/img/IAM.jpg)\n",
      "\n",
      "All datasets have imperfections. We need to understand the imperfections of each dataset before proceeding with data analysis.  We'll continue working with REDD but please note that we're not picking on REDD! All datasets have issues!"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```python\n",
      "import nilmtk.stats.electricity.building as bstats\n",
      "```"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Gaps"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are two reasons why data might not be recorded:\n",
      "\n",
      "1. The appliance and meter were unplugged from the mains\n",
      "   (hence the appliance is off when the appliance monitor is off).\n",
      "2. The appliance monitor is broken (hence we have no reliable\n",
      "   information about the state of the appliance).\n",
      "\n",
      "In the plots below, a dark rectangle shows the presence of a gap. A gap is defined as any pair of consecutive samples further apart than 4 times the  sample period for that meter.\n",
      "\n",
      "#### Plot gaps using rectangles"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bstats.plot_missing_samples_using_rectangles(electric)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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0mpSo+3fA7ol6MhF6bj4MfACYFHqfYrqBR7PKdBzHcZyJRtv14JjZOkmPS+o1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\nSDpX0rnAY0BPQp6FvxsSZYrreyjIXQ/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Ag4SxMieY2XmSTgXmS3o8Lj+P0Gtzv6TvAA8Q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\nRPMAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f2194cee3d0>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The advantages of `plot_missing_samples_using_rectangles` are:\n",
      "\n",
      "* clearly shows large gaps\n",
      "* shows all data so can be zoomed in to your heart's content\n",
      "\n",
      "The disadvantages are:\n",
      "\n",
      "* The choice of `max_sample_period` is somewhat subjective\n",
      "* Because it plots lots of rectangles, it can be slow to plot.\n",
      "\n",
      "To overcome both of these disadvantages, we have a sister function:\n",
      "\n",
      "#### Plot gaps using bitmap"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bstats.plot_missing_samples_using_bitmap(electric)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Seconds per pixel = 3917.6\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f2191f84550>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2mJnZ8PLpgyEm6RBJmw53O/qwNnAdLbKtzMxseBUKjf+1upXSUyDpI8CXgO8D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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f2194ca5410>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Proportion of energy submetered for house 1"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bstats.proportion_of_energy_submetered(electric)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating proportion of energy submetered...\n",
        "Masking appliances with mains... may take a little while..."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mains sample period = 1.0, max_sample_period = 20.0\n",
        "Getting gap starts and ends...\n",
        "Found 27 gap starts and 27 gap ends."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "done\n",
        "Inserting zeros... may take a little while..."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "done inserting zeros\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "0.76004797563554483"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Proportion of energy per appliance"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bstats.proportion_per_appliance(electric)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Masking appliances with mains... may take a little while..."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mains sample period = 1.0, max_sample_period = 20.0\n",
        "Getting gap starts and ends...\n",
        "Found 27 gap starts and 27 gap ends."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "done\n",
        "Inserting zeros... may take a little while..."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "done inserting zeros\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "(fridge, 1)             0.143596\n",
        "(lighting, 1)           0.112564\n",
        "(washer dryer, 1)       0.093916\n",
        "(kitchen outlets, 2)    0.073981\n",
        "(dishwasher, 1)         0.066409\n",
        "(microwave, 1)          0.058262\n",
        "(kitchen outlets, 1)    0.055435\n",
        "(lighting, 2)           0.046542\n",
        "(oven, 1)               0.036079\n",
        "(lighting, 3)           0.034429\n",
        "(bathroom misc, 1)      0.018303\n",
        "(kitchen outlets, 3)    0.014565\n",
        "(kitchen outlets, 4)    0.005331\n",
        "(space heater, 1)       0.000334\n",
        "(hob, 1)                0.000302\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Statistics for a single appliance"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```python\n",
      "import nilmtk.stats.electricity.single as sstats\n",
      "```"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "APPLIANCE_TYPE = 'oven'\n",
      "appliance = electric.appliances[(APPLIANCE_TYPE, 1)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Sample period"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sample_period = sstats.get_sample_period(appliance)\n",
      "\n",
      "print('average sample period = {:.1f} seconds'.format(sample_period))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "average sample period = 3.6 seconds\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Dropout rate over entire timeseries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dropout_rate = sstats.get_dropout_rate(data=appliance)\n",
      "\n",
      "print('average dropout rate = {:.1%}'.format(dropout_rate))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "average dropout rate = 13.5%\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Dropout rate per period"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sstats.dropout_rate_per_period(data=appliance, rule='D').plot()\n",
      "\n",
      "ylabel('dropout rate')\n",
      "title('Dropout rate per day for ' + APPLIANCE_TYPE)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "<matplotlib.text.Text at 0x7f2192029850>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+fxCv5upwAfA7wHMA1tqJ5Rdr6TK5ElPZIh2pxMIbi4RYJlekNRkjGo00uihA\nRXO6grjIihlZoCZeTRD/F+CGRgfvSkOjGQVxaXqZgCxD6ku1xEkmompOF1khjuMwMr78xLbvWWvv\nq3zC6yNvmIXuTESaQSZXDEx/OEAkEqGnvUU1cZEVMp4uUCyV592mmivEc8aYL+AOKcvhzp/+JuDr\nC+3ojSe/ETgCONba986x3ZuAzwEd3gIr81qoo18k7BzHIZMrsaEvOEEcoKsjye7BMcplJzDN/CLN\naqH+cKiuJv4mIA1cAVzj/du80E7ekqWfBG6y1r4HON8Yc+1JtjsLOKuKckwbHsssvJFIiOXyJcqO\nQ2tAhpf5etqTOA5MpFUbF6m3alqdq7nNf7+19pOVTxhjrq9iv8uBvdbagvf4HtzFVG6vOE4bbrb7\nb+JmwVdFNXFpdlNZ988mSM3pAN0dbnLb6GR++nsRqY+RWtTEZwdwTzXjxAeAymS4ce+5Sn8JvKci\n0C/YPteeSiiIS9NLZ4M1W5uv21tXXMltIvVXTRCf8wphjLljnv1OB/5pgWMfBjorHnd7z/nH3wz0\nAD9rzPSKpr9vjLnZWvvgXAdd19vG4NAka9d2EIlU1yfX39+58EbL3CeI5whimVbiHEEs02L3eXrv\nUQD6etqq3m8l3veWDV0AlCPRupUraD+LoJ4jiGVaiXMEsUz1OseUN+HTfOa7zZ8APgTcAGSBu3Fr\nyi8FdlVRxvuAbcaYpLU2j9un/nFv7vWitXY/8Cv+xsaYvwb+fqHEtnW9bTw3OMZz+47S1ZZcsBD9\n/Z0MDS1udNxi9wniOYJYppU4RxDLtJR90hm3Ju6Uy1Xtt1LvO+pNtPTCofG6lCuIP4sgniOIZVqJ\ncwSxTPU8x4EjkyTi8zeYz/fqb1tr7wQ6rbXvttZ+x1r7bWvtu3GD+ry8YPzbwEeNMe8DHrHW3oE7\n29v0OuTGmLXGmD8FHODtxpiN8x13YE0boGFm0twC2yfuN6drERSRuhsZz07PlDiXOa8Q1tpB79sX\nGWNarLU5AGNMK3BeNQWw1t7KrNXOrLXvmPV4GHi/929BA73uOsZDoxm2e017Is0m7QXxoCx+4vOT\n2bQIikh95QolJjMFtq3rmHe7am7zvwzsMcY8gNucfgluQlpDrO9tB1QTl+Y2lQlmYltnKkE0EtGE\nLyJ15o8RX6gmXk12+seAV+HWqL8DXGet/cflF3FpBnrd5nRlqEszSwe0OT0ajdDZntCa4iJ15mem\n93UvsTlwwRPsAAAgAElEQVS9krX2UeDRZZeqBgbWuM3pCuLSzPw+8aCsYFapp72FgyNTOI5T9QgR\nEVkcv7W5b7k18aBpa03Q3hrXrG3S1KbHibcGL4h3dyTJF8vT652LSO35C58suzk9iNb2pBgey2pd\ncWlaQW1OB034IrISjlbZnB7OIN7dSqFYZjxdWHhjkRDya+KtyWBlp0NFhrqS20TqZmQsSwRYs8D0\nxqEN4qCFUKR5TWULJBNR4rHg/Yn2dLg18VHVxEXqZmQ8S1dHclmTvQTW2m4vuW1UyW3SnNKZYuCG\nl/n85vRx1cRF6qJcdjg2kWPtAv3hENogrpq4NLepbCGQ/eFQsZKZJnwRqYuxqTylsrNgUhuEPIhr\nwhdpRo7jkM4WAlsT79HUqyJ1NT1GvFmDeN90TVxBXJpPoVimWHICG8S7O/zsdNXEReqh2sx0CGkQ\nb03G6UglGFIQlyaUyXljxAOYmQ6QiMdoa4krO12kTvxW5t6u+TPTIaRBHNwm9ZGxLGWNFZcmk8m7\nk6gEtSYObm1cU6+K1EfTN6eDO+FLsVRmXE160mSma+JBDuLtSaayRQrFcqOLItJ0pqdcbdbmdKjM\nUFeTujSXtBfEg5qdDtDjZajrJlqk9kbGc7QkY1VdA8IfxEc1zEyaSyYbgpq4JnwRqZuj41nWdrVW\ntcBQ+IO4auLSZMLRnO7VxJXcJlJTmVyRdK5Y1RhxCHUQ15Kk0pxCEcSna+IK4iK1NJPUtnBmOoQ4\niPdNT/ii5nRpLjN94sEcYgaa8EWkXhYzRhxCHMRbEjG62jRWXJqPv053ENcS93X5K5mpJi5SUzNj\nxJs8iAP0dac0VlyazsxkL8EN4v5KZprwRaS2Rsbd1q1qxohDyIN4f08rpbKjC4k0lTD0ibe1xInH\noprwRaTGji5iohcIeRDv02pm0oQy+eAH8UgkQnd7Us3pIjU2PJ4lGonQ05msavtQB3GtKy7NKJMr\nkohHScSD/efZ05FkfCqv7iyRGjo6nmVNZ5JYtLq//2BfJRagdcWlGaWzRdpbE40uxoK6O1oolR0m\nM4VGF0WkKRRLZY5N5KpOaoOmCeKqiUtzKJbKjIxn6V+TanRRFtTtDTPThC8itTE6mcNxqu8Ph5AH\ncf+NKohLszhyLEOx5LBtfVeji7IgTb0qUltH/cz0KseIQ8iDeDIRo7s9qeZ0aRoHhqcA2Lq+s8El\nWZi/CIpGh4jUxmLHiEPIgzi4TepHx3OUy0qukfDbPzQJhCOId/mztilDXaQmFjvlKjRDEO9JUSo7\nGq8qTcGviYehOd2f8EV/eyK1sdgx4tAMQVzJbdJEBoenSLXEFtUn1ijTK5mpJi5SE8Pjq7A53b/Y\nDWldcQm5QrHM4aMZNq3tqGod4Ubrak8QAUbVJy5SE0fHc7S3xhc10VPog/ja6dXMVBOXcDt0NE3Z\ncdi4tr3RRalKLBqlsy2hlcxEasBxHEbGs4uqhUMTBPF+rSsuTWJw2E1q29QfjiAO7oQvSmwTWb6p\nbJFcvrSo/nBogiDe26VZ26Q5+Eltm0JSEwd3wpdsvkQuX2p0UURCbSlJbdAEQTwRj9LTkVRNXEJv\ncMgL4v0dDS5J9TThi0htTI8R765+eBk0QRAHdyGUo+M5SuVyo4sismSDw1N0pBJ0tQV/3nSfn6Gu\nCV9ElmdktdbEwU1uKzsOxyZUG5BwyhVKDB3LsGlteygy031+TVz94iLLMz3l6qoM4j3KUJdwOzSS\nxgE2hiipDWamXtWELyLLs5Qx4tAsQVwZ6hJy/nSrYUpqg4qVzFQTF1mWo+NZYtHIdOtWtZoiiGvC\nFwm7MGamQ0Vim2riIsvijhFvIbrI7rSmCOKa8EXCbnA4fJnpMFMTV2KbyNIVimXGJvOL7g+HJgni\nfV2tRFBzuoTX4NAU3e1JOlLhyUwHaE3GaUnGlNgmsgzHJpaWmQ5NEsTjsSg9nS0K4hJKmVyRkfFs\naKZbna2nPampV0WWYcTLTF9sUhs0SRAHb13xiSzFksaKS7gcGPGb0sMZxLs7WphIFzRPg8gS+V3B\nS1m9sKmCuOOgseISOgeGwpnU5utuT+IA41OFRhdFJJSWOuUqNFUQ1zAzCaewJrX5ZiZ80Q20yFLM\njBFf3JSr0FRBXAuhSDj5QXxjXzhr4jMTvii5TWQpji5xohdoxiA+qpq4hMvg0CS9XS20tcYbXZQl\n0YQvIsszMp6jsy1BSyK26H2bJoj39ag5XcJnKltgdDIf2sx00IQvIsvhOA5Hx7NLqoVDEwXx3s4W\nIhEYUXO6hMhgyJPaAHr8lcxUExdZtIl0gUKxzNrVHsTjsSi9nS3TCQIiYTAz3Wo4k9oAujo0a5vI\nUo0soz8cmiiIA/R1pzg2ntNYcQmN6Zp4SMeIA3SkEsSiEU34IrIE02PEl5CZDlD3TBpjzCuAG4Ej\ngGOtfe+s198BrAMOApcAf2atfWYp51rb3Yp9wc30G1jTtsySi9Tf4LC7ellYM9MBopEIXe1JNaeL\nLMH0GPElTPQCda6JG2PagE8CN1lr3wOcb4y5dtZm7dbaP7DWfhD4EvDBpZ5vZpiZmtQlHA4MT7G2\nu5WW5OKzUoOkuz3J6GQex3EaXRSRUFnqOuK+ejenXw7stdb6UzndA1xfuYG19s8qHsaAiaWeTBO+\nSJiMp/OMpwtsDukkL5V6Oloolsqkc8VGF0UkVI5686YvZbY2qH8QH+D4oDzuPXcCY0wS+CXgT5d6\nMk34ImHiT7ca5uFlvm4lt4ksych4lkQ8Smfb0lYwrHef+GGgs+Jxt/fccbwA/gngT6y1zy900P7+\nzpM+X465TZKT2dJx28y1/VLOUavtV+IcQSzTSpwjiGU62T73PzMEwFmn9p30eGF63xv85+OxE7bR\nzzsY2zfLOYJYpuWc49hEjoE1KQYGuhZ9Tqh/EL8P2GaMSVpr88AVwMeNMWuAorV2wus3/zjwQWvt\nk8aYN1hrvzTfQYeGTt7i7pTLRCMR9h+ZmN6mv79zzu3nsth9gniOIJZpJc4RxDLNtc8ze44C0NkS\nO+G1sL3vhNemt3dwlI09rVXtU+8yNfM5glimlThHEMu0nHPkCiXGp/Js7m9f9Dl9dW1Ot9amgd8G\nPmqMeR/wiLX2DuCd3vMA/47bd/5xY8wdwNuXer5YNEpvV8t0yr5IkA0OTRKJwIa+8I+k6GlXc7rI\nYi1n9TJf3YeYWWtvBW6d9dw7Kr7/yVqeb213K8/sG6VQLJOIN9UweGkijuNwYHiKgTVtJOLhzkyH\niglftJKZSNVGahDEmy7K9XW34jBzhyMSRKOTeaayxVBPt1ppeupV1cRFqjadmb7EMeLQhEFcw8wk\nDGamW22OIN7lN6drwheRqvldv0sdIw5NGcQ1zEyCb3DInaktzNOtVkrEo7S3xrWSmcgizDSnL23K\nVWjqIK6auATXYJPVxMGd8EXN6SLVOzqeJQKs6VRNfJqa0yUMDgxPEYtGWNcb/sx0X3dHknSuSKFY\nanRRREJheCxLV0dyWUnYTRfE13S2EItG1JwugeU4DoPDU6zvbSMea54/wW4NMxOpWrnscGwit6zM\ndGjCIB6NRujtalFNXALr6HiObL7UFNOtVurucPv1RpXcJrKgsak8pbKjIH4ya7tTjE3myRfUrCfB\n4y8/2ixJbT5N+AKlcpk7Hh7U2uqyoFqMEYcmDeL+mLsRjRWXAGrGpDbQhC8A9z95mM99+xn+7ZtP\nNrooEnBHp5cgXXpmOjRpEPcz1DX9qgTRYBOtXlbJn/BldBXXxHc+dQSAOx8eZCpbWGBrWc2ma+LL\nmOgFmjSI9ytDXQJscHiKeCzKwJpUo4tSU/5ypOOrtCY+mSnwxPNHiQD5QokfPHao0UWSAPMrmWpO\nPwn/zmZIGeoSMGXH4eDwFBv62ohFm+vPr3uV18QffOYIpbLDqy/bSjwW5Y6HB3Ecp9HFkoCqxZSr\n0KRBXM3pElTDoxnyxXLTJbUBpFpiJOPRVTv16v1PHgbg2os289IXb+TQ0TRP7z3W4FJJUI2MZ2lJ\nxmhrWd46ZE0ZxHs6/LHiCuISLH5/eLMltQFEIhG6O5KrMjN7dDLHM/tGOX1zN33drbz28u0A3PHw\nYINLJkE1Mpalr6uVSCSyrOM0ZRCPRiP0dbeuSBB/+NkhPvXlRymVy3U/l4TfTGZ6R4NLUh/d7S2M\nTxUol1dXM/IDTx/BAS47ax0AZ56yhs39HTz87LDmk5cTpLMF0rnisvvDoUmDOLhN6uNTebL5Yt3O\nkcuX+Lebn+ab9zzPPUpikSr4q5dtbMLmdHCT28qOw2RmdWVm73zqMJEIXHLmAOC2Slxz0SZKZYe7\nHjnQ4NJJ0Awdc/O1lrPwia+pgzjMfFj1cMfDg4yn3YvVV7//vOaMlgXtH5oimYhO/342m5lhZqun\n9jk8mmH34DhnbVszPfUswEvOXkdLMsadPzqgljo5ztCoG5eWswSpr2mDeJ83zOzw0XRdjp/Ll7j5\n/r2kWmJcd9k2jk3kuOMh9X/J3ErlMoeOTrGxr53oMvvBgmpmwpfVk9y282l3bPgOryndl2qJc8U5\n6zk2kePRXSONKJoE1NAxNy4tNzMdmjiI93sfzpFj9Qnitz+8n4l0gVdesoVfvv5sUi0xvnHvXjK5\n+jXfS7gdOZahWHKaMjPdtxqnXt355GFi0QgXmf4TXrvmwk2AEtzkeEemm9MVxOfkL0l6pA418Wy+\nyM337SPVEueVl26hqz3Jq3ZsZTJT4LsPvFDz80lzmMlMb86kNphZBGW1TL16cGSKfUcmOXd7Lx2p\nxAmvbx7o4PTN3Tz+/NG6VSgkfPxu3uVOuQpNHMT9ZopDdQjidzw0yGSmwCsv2Ux7q/uH+8pLttDZ\nluCWnftWXVKPVMfPTG+26VYr+X3Cq2XCF3+a1cvOXjfnNn5t/HsPK8FNXEOjaSIRd+ns5WraIN7d\nkSQei9a8Jp7NF7n5frcWft2lW6afT7XEuf7yU8jmS3zr3r01Pac0Bz+Ib27m5vRV1CfuOA47nzpM\nMh7lxWesnXO7S140QEcqwfcfO6jkVwHc5vQ1nS01mbWxaYN4NOKOFa91E9btXi38uku30NZ6fPPZ\nNRdupLerhdse2s+xidXRnCjVGxyaJNUSq8ndd1B1tiWJRFgVE768cGSSgyNpzj99La3JuWfdSsSj\nvOyCDUxmCjzgJcHJ6lUqlzk6lqlJfzg0cRAHd5jZ2GSeXL42d7+ZXJFb7t9HW0ucV16y+YTXE/EY\nP37ldgrFMl+/5/manFOaQ6FY4sixDBvXti97hqYgi0YjdLUlV0Vi2/1PudOsXnbWwILbXv3iTURQ\ngpvA6ESeslObpDZYBUEcYLhGC6Hc/tD+OWvhvivPW8+63jbufvQgh5XIIp7BoSlKZacpp1udrbsj\n2fTN6Y7jsPPJI7QmY5x3at+C2/f3pDj31D52D46z7/DECpRQgmpkeh1xBfEFre9tA9wm8OWuJlRZ\nC3/FJVvm3C4WjXLjy7ZTKjt89W7VxsW179A40NyZ6b7u9hZyhVJTD7d87sA4I+NZLjL9JBOxqvaZ\nSXBTbXw1q9U64r6mDuIvv2Ajp2zo4o6HB5c99Ou2B/czlS3yqh1baGudf9WZS84cYOtAB/c/eZgX\njkwu67zSHPYdcmtfzTrdaqXuVZDc5q9YNnuCl/mcf1offV0t3PvE4aa+wZH5zawjXpvcmKYO4qmW\nOH/2ay+huyPJF27fxYPPDC3pOJlckW/v3Ed76/y1cF80EuEnrzoNB/jfu55b0jmluez1auKbV0Fz\n+nSGepMmt5XLDg88fYT21jhnn7Km6v2i0Qgvf/EmcoUS9z6htRZWq6NqTl+c/jUpbvqpC0gmYvzz\n15/guQPjiz7GrV4t/LodW0lVufbreaf2YjZ386Ndw+waHFv0OaW57Ds0QXtrnK6KubWbVXe7P+FL\nc9bEn3lhlLGpPJecOUA8trhL6MvP30AsGuGOh5ffxSfhNDLu3twqsW0Rtq3v5Ldefw6FUpmP/s8j\n05PPVyOdLfIdvxZ+8YkZ6XOJeLVxgC/fuVt/sKtYvlDi4MgUm5o8M93X7BO+7Hxq8U3pvu6OFi5+\nUT+DQ1M8u18396vNsYkc+w5P0J5KVF0hXMiqCOIAF5y+lje90jCeLvCRLz7CVLa6WdVue/AFry+8\n+lq4z2zp4fzT+nh63yhP7jm2lGJLEzg4ksZxYFN/8ye1AfQ08dSrxVKZHz59hO6OJC/a0rOkY2g+\n9dXp8NE0f/W5BxmbynPDldtrdtxVE8QBrr1oM9dduoWDI2k+/uXHKJbmXx4wnS3y7Z0v0N4a58cW\nUQuvdOPLTgXgS6qNr1qDw25yYzNPt1ppeiWzJqyJP7nnKFPZIpeeOUA0urRWFbOlh41r2/nh00cY\nb9IuBzne3kMT/NW/P8jIeJafeNl23vTqM2t27FUVxAF++trTudj08/S+UT5z89PzBtZbH3yBdK7I\nqy9bfC3ct219JzvOGmDPoQkesktLrJNwWw3TrVaaWcms+Wri9z/pzZW+hKZ0XyQS4eoXb6RUdrj7\nUc2n3uye3nuMv/nPh5hMF/jF6ww/fuX2mnarrbogHo1E+PXXnc32DV384PFDfO2ePSfdLp0t8J2d\nL9CRSnDtRUurhft+4mWnEo1E+PJdz1Euqza+2virl62WmngyESPVEm+6xLZ8ocTDzw6xtruVUzd2\nLetYV5y7gWQiyp0/OqBrQhN7yA7x9//9CIVimd98/Tlcs8xYcjKrLogDtCRi/N5Pnc/a7la++v3n\nueexgyds890f7l92Ldy3vreNl56/noMjaQ0tWYUODE/R09lCZ1vzZ6b7utuTTZfY9thzI2TzJXac\ntW7ZNam21jgvOXsdw2NZHn9+pEYllCC5+5EDfPx/HyMWjXDTGy9YUiJkNVZlEAf3InPTGy+grSXO\nZ25+mqf3ziSeTWYKfOcBvxa+qSbn+/ErtxOPRfnK3c9TKM7fFy/NI5svMjyWZeu6zkYXZUX1dCSZ\nzBQWzDsJk/u9ZUd3VDFXejWuudCtld3xkBLcms3N9+3l0zc/TXtrgrf93IWcs723budatUEc3ObN\nt/zkeQD845cf44DXd/m1u3aTyRV5zWVb512daDF6u1q59qJNjIxnuesR9YM1u2KpzKO7h/mXbz4F\nwLYNy2t+DZtuL0N9tElW88vkijyya5gNfW1sGajNKINt6zvZvqGLR3ePcLjGSyZLYziOw3/fvosv\nfm83azpbeOebLlp218tCahOhQuzMbWv41deexT9/40k+8sVH+P2fvoCv3bWbjlSCa2pUC/e99vJt\n3PnIAb7+gz1cfelW0uk8iXiUeCxKLBpp2jHEhWKJw8cyDI1m6DkyRXoqRyIeJRGPkvS+JuKx4x6H\n8bMoOw67B8e478nDPPDUESYz7jDGgZ4UV9ehLyzI/LHiR8ezrEnV/jJTLJUZGctyYDTL8MgU5bJD\nqVymVHYolR3vsfevVJ5+XHYcLj5nA+s6k4v6HfvRrmEKxXJNmtIrXXPhJv714DhfuXMXr3vJVhLx\n6uZhD7vnDozz1e8/TyQa4WXnbeDCM9YuOds/KErlMp+5+WnueewQ63vb+MOfeXHN5kefz6oP4gCX\nn7ueodEMX/n+87z3Mz8kVyjxxmtOq1kt3NfVluRVl27ha/fs4f/81a3HvRaJuOsOJ2JR4t7XRMXX\ndWvb2dzXzmmbujhlfWfN/tgn0nn2HJpwJyBobyHqOHS1J+lqS9LVnqCrLVnVAg+O4zA6mefQyBSH\njqY5eDTNoaNpDo2kGRnLstjUnXjMDegtyRixaIS4/3mc7DPyboQS8Si9PSn6O1s4bVP3iq3bvX9o\nkvufPMx9TxyeXtygqy3BKy7ezEvOWc/2DZ0MDHQxNLR6Vq/y508fncgtOYiXyw7D41mOHE1z+FiG\nw/7XY+7vVGmJCWFfuvM5Tt/UzQ1XbOO8U/uqCso7p+dKr01Tum/HWQN84fZn+cb3n+eWe/dwyvou\nTt/UzWmbujl9c/f0zVCzGBrN8KU7d7PzqZl11R/dNcy6NSletWMrV5y7vuoFZYIkVyjx8S8/zo92\nDbN9Qyc3vfGCFcuBURD3vO7KUxgazXDP44fo7khy7YX1qTm9+rKtpHNFCiWYTOcoFMvuv1KZove1\nUCxTLJWZzBSmX99dMV1sLBph67oOTtvo/rGftqmLvq7WBS9GU9kCew5NsOfguPd1YjrozKc1GfOC\nepLOtgTd7Uk625J0dLTw3P5RDo2kOXQsfdJ127vak5yxpYf1vW2sW5Oio6OV0bE0ef99e//yxVLF\n9/7zJcoO5PJF0tmC99k4Vfez9nW1ctqmLveCuKmbLQMdi54mcy4jY1nuf8oN3PuHJqc/pyvPXc9l\n56zjrG1riEVXb29Vjzf16p0P7+fZ3jZwnOkbOX9Up4OD99/0UM8SEfYMjnH4WJqh0QzF0omBuiOV\n4JQNnaxb08bWDd0U8gViUbc1KxaLEI1G3Bu/aHT6e/+1Uslh5zND3P/EIT7yxUfZuq6DGy4/hYtM\n/5w1wYl0nsefP8rWdR1s6KvtCINkIsbbfu5CfvjsMI/tGua5A+PHTdM80JPi9M3u7+/pm7vZuLad\naAhbqSYzBb7xgz3c/tB+iiWHbes7+ZlrTueULWv4/C1Pce8Th/jst5/hf+9+jh+7eDPXXrSZjtTJ\nl3sOmnS2yIf+6V6eeG6Es09Zw+/eeF7NZmOrhoK4JxKJ8MuvOZOezhYuO28jLcn63A22JuP8/CsM\n/f2dVdfMHMeBeJydjx1g9+AYuw+Mse/wJM8fnODWB/cDbs3HDepdnLaxm0Rrkqf3HnOD9aFx9hyc\n4Mis6WY72xKcd2of2zd0sm1dJ7297ew/OMb4VJ7xdN77WmBiKs9YOs9zB8Ypn2RcfSIeZd2aNtb3\ntbG+t431vSnW97azvjd1wrrri3nfc21fdtwmUvfmx6Hg3QAUSw6xRJyHnz7E7kH3YrjzqSPTd/2J\neJRT1ne6Nz4buzl9U9d0362vUCyTzhaYyhZJZ4tMZQuzvhY5cDTNE8+5GcXxWIQLz1jLS85ZzwWn\n9YWyFlEPA2tSANy1xFnJ2lribBnoZF1vinVr3BvAdb1tDKxJ0V7xO7XY3yeA6648lYeeOMg3793D\nA08d4RNfeZwNfW289iXbuOzsdSfc6N372EFKZWdZY8Pns3VdJxefu5GhoQmy+SLPH5xg1+AYu/aP\nsXtwjB88fogfPO6Oakm1xDltUxcXmAE2rklx6sYuWgL8O1colrntwf184wd7SOeK9HW18oarTmXH\n2euIRiL093fyK689ixtffiq3PbifOx4a5Ct3P8+37tvLy87byHU7ttDfk2r02zhO2XEYHs0wODTF\n/uEpdj51mMGhKS55UT//53XnkIiv7M27gniFeCzKG646bUkXhnqKRCL097Zx2dnruOxs90KSL5TY\ne3iC3YPj7B4cY9eBMR6yQ3NOKNPeGuecU9Zwyga3Of6U9V30drUcV3vv7+9k29q2OctRdhzS2SLj\nU3km0nk6OltpibpJeytZO4hGIkTjsZN2KfT3d7JxjdsP5TgOR45l2DU4xu4D3uc0OHbcnNVru1tp\nSyUYn8yRzhbJVzFyIBKBM7f28JJz1nPxi/qPCyriOnVjF2//uQuJt8QZ91p7It7/Iu7/pp+LeC9E\nIrBxXRfJiENHKlHXvIgtAx381uvP5SdeluZb9+7l3icO8S/ffIqvfv95XnPZVl56/obp36+7HnZv\nlC89s7ZN6SfTmoxz1rY1nLXNXR2t7DgcHJ5yg7oX2B9/7iiPP3cUcFvltq3v5IzN3ZyxuYfTN3fT\nFYChjGXHYedTh/nync8xPJalrSXOT19zOj928aaT/t32dLTwhqtO47Uv2cbdjx7kuw/s47aH9nP7\nw/u59MwBXn3ZVk5Zv7LJoY7jcGwix+DwFINDUwwOTTI4PMWBkSnyheOvE6+54hTe8NLtDenXVxAP\nqWQixhmbezhjszt/s+M4HB3PsfuA+8c+OlVgbVeLG7A3dNHfvXBz+0KikQgdqYTXzNUeuJud2SKR\nCOt621jX28aV520A3CzjPQfH2eUF9ecPjpPNl0i1xNiwtp321jhtrQnva5x27/v21sT049NP6aOQ\nba4x0LUWiUQ4c9uamrS81NP63jZ+9fqzeP1Lt3PL/fu469EDfO47lq/9YA+vunQrF5m1PLZrmNM2\ndbG2ATXCaCTCpv4ONvV3cNWL3UTb8XSeI+N5HnryEM/uH2XPoQmeOzDOt3e+MP2ezJZu7/rQTX9P\natF/+8WS2521FM/sO8YXbt/FnkMTxKIRrrt0CzdccUpVzeOpljjXXbqFay/axANPH+GW+/dNt6ad\nubWHn7zW0BKFVDJGa0uc1mRsSV1kpXKZqUyRiUyByXSeiXSByUyBiUyBdL7E7hdGGRyeOmHd93gs\nwoa+djb1t7NpbTub1nawub+ds84YaNi1UEG8SUQiEfq6W+nrbmXHWesCH2AbJdUS56xTejnrlJlx\nm4v9rHo6WxhSEG8qfd2tvOk6ww1XbOM7D7zA7Q8P8t937OJLd+6m7CxtxbJ66WpLctq2Pk5f7w51\nyxVKPH9gnGf3j/Lsfvcm/q5HDnLXI+4kVt3tSc7Y3E3fmjbGJ3LkiyXyhTL5Qmnm+1lf/aTBlmSM\nTu/GvaMt4X2frPg+QWeb+7VQKvOprz3Jzifdpv8dZw3wk1edxsASbn7isSiXn7Oel5y9jif3HOOW\n+/fyxJ5j/NVndp6wbTIepbUlTqolTirpzhbYWvE1Fo8xdDQ9HaQn03mmssWTnHVGNBJhXW+Kc7b3\nesHaDdwDa1KBy3VREBcR8XR3tPDGa07nNS/Zxm0P7ufWH75AqeywYwWa0peqJRHjzG1rONNrgi+V\ny+w/MjUd1O3+UX74zMm72fxhncmEW7PtbndHoyQTURKJGMfGskxkCuwfmqo6odRs7uanrz2jJuOj\nIzurFmgAAAkbSURBVJEI52zv5Zztvew7PMFT+8cYHkmTyRfJ5Ipk8yUyuSKZfIlsrsixiewJTd2+\naCRCR1uCno4Wtgx0eDcmSfdmxL8paUuwfUsvSZwV79teKgVxEZFZOlIJXv/S7bx6x1baO1sp5qpb\nujgIYtEo29Z3sm19J6+4ZAuO4zAylqWjK8XURJZkwg3aiXh03lyWyhYqx3HIF8pMZPJMZgpMpv1a\nrfc1UyCbL/JjO7Zx6kB7XfIZKhMA51Mslcl6QT2TL7F+oJN8Nk+qJV5V7k7YWjEVxEVE5tCSjLGm\nq5WhofAE8dkikQhre1JucFpi5TISidCSjNGSTLG2e+7m8SAEwHgsSkcqOt0H39/f0fAy1VM42gtE\nRETkBAriIiIiIaUgLiIiElIK4iIiIiGlIC4iIhJSCuIiIiIhpSAuIiISUnUdJ26MeQVwI3AEcKy1\n7531eivwd8B+4AzgA9baZ+tZJhERkWZRt5q4MaYN+CRwk7X2PcD5xphrZ212E7DHWvsB4MPAv9Sr\nPCIiIs2mns3plwN7rbX+VEf3ANfP2ua1wL0A1trHgQuMMR11LJOIiEjTqGcQHwAq57ob955b7DYi\nIiJyEvXsEz8MdFY87vaeq3QEqFzqpst7bk6ResysLyIiEkL1rInfB2wzxiS9x1cA3zTGrDHG+MH9\nm7jN7hhjzgN+ZK2drGOZREREmkasXgceGRkp9PX1PQX8YV9f32XAAWvtv/X19b0HOHdkZOSevr6+\nB4Gf7uvruxC3v/yPRkZGjtarTCIiIiIiIiINp/7lVcgYcxrwPuAhYDMwYq19nzHmL4CrKjb9S2vt\nrQ0oYqidbH4EY8yHgSlgErgAd+jl7BwRWcAcn+2FwO8BTwLnAO+21r7QwGKGjjFmPfB+4Hxr7Q7v\nuSTwDmAMeBFwzFr7p40rZfjMc619K3AuYIErcedIuW8p56jrZC8SWGuAz1trvw5gjHnCGPNN3Ivi\nNY0tWrhVzI9wtrW2YIz5H29+hAlr7Z9527wdeBdu4JEqzfPZfgh4s7X2EWPMDcDHgJ9oZFlD6Erg\nK7g3mL53AI9aa78K03lLsjhzXWuTwFustTljzLPAe4HrlnICBfFVyFr7w1lPRXFriRhj/gTI4eZL\nfMxam1nh4oXdSedHsNb+YcU2MY4fWinVOdlnewPubI/7vOeeB2ZPKiULsNZ+yRhz9aynfx4YMcb8\nHrAW+McVL1jIzXGtnbTWfrDiuTOAJ5Z6jkDPnW6MWW+M+f+NMTsrnrvYGPPfxpi3e18vbGQZw84Y\ncyNwi7X2GeCLwIettR/CDTIfa2jhwmneuQ+MMT3AK4EPIot1ss+2H7gbb5QLsANoN8YE+toWEtuA\npLX2o8DtuNcHWaKKa631Hq8zxvwD7o3o+5d63KD/ovtNPJV99+8H/sNa+7fAfwJ/2YiCNQNjzDXA\nVdba3wew1j5ZUfO+A9VolmLO+RGMMd24tZlfsdaONqBsYTfXZ/tLwOVejdHBHQlTbkD5ms047lBh\ncGfWvNIYozyqJZh9rQWw1h621r4V+AvgW0s9dqCDuLX2S7iJQJUOMVOzGQAOrmihmoQx5nrgOmvt\nTcaYjcaYy40xf1uxyRnArgYVL8zmmh9hLfBx4O3W2r3GmDc0rIThddLPFthgrX23V2M8AHy2UQVs\nMrcBp3rfbwV2WWudBpYnlOa41v5RxSZ7mPmcFy3wd1VeP80HrbWXeo83A5/H/YPeAfyCMlEXxxhz\nMfA94AHc34F23ADzIqANN/P3PNwsXwXyRfIyqH8KGALyXjbqg7h94ce8zcatta9vVBnDao7P9l24\nF8EncWvnf2mtzTWwmKFjjHk5bovGq3CTB/8e6AX+Gnga96b+Eyfp45V5zHOtvRjIA8O4yYSft9Z+\nrUHFrC9jzNXGmAcqHj9kjLnM+/5cY8zTjSudiIhI4wS6OX0Om3Gb1PG+bmpgWURERBqmbtOu1oLX\nxPOLwIv7+vpSfX19DwBPAW/t6+szuM0/fzcyMrLk9HwRERERERERERERERERERERERERERERERER\nEREREREREREREREREREREREREREREREREREREREREZFwiDS6ACJBYYx5GfA+4BTgDGttoeK1vwF+\nAXi3tfZfG1NCEZHjBXo9cZGVNDIysq+vr28bsAGIjYyM/BDAGDMAvBXIWWt/p5FlFBGpFG90AUQC\n6H38v/buH1SuIorj+Pc9tBD1QRwREkIUxFPJU4xF0IAErEwhdhYmWAjBRp5KIiRVFDVCSKEQCy20\nsjCgkCgJIgkWCiJBgij+RFEQDJFRiP86tbg3uMZXWCS7D/b7aXaZYS4zxe5wmDPnwuGqenWMxh8F\nDgNPAVTVK8D3wDXAD0kOVdVjwNMM0fr7wJvAmSR7Z7EASfNhcdYTkNagz4CPgF1jFP4n8ONE/9Ek\n+5PsBnZU1dVJXgQ+ZdjU/wC+cwOXdLkZiUur2w+cADYBB4Dlib4NVfUscB5YAhrwG/ASsFJVB4DP\npztdSfPISFxaRZIvgA8YzsF/utBeVcvA7iT7krwAnOWfBNG3gC3AHuC16c5Y0jwysU0aVdVm4BFg\nY2vtZJIjvfeTVXU98ARwO3AGWN9a29ZaK+BeYKH3fqr3/ldr7SpgMcmxmS1EkiT9f1V15fj5ZFXd\nNOPpSJoTnolLl8ZDVXUncDbJt7OejCRJkqQ1zIptmntVdTPD3fDTwEagJ3mmqq4Dnge+AW4B9iY5\nN47ZDBwCPh6vml141hKwD9iZZP10VyJp3pidLsE64I0kB5OsAA9W1R3Ac8B7Yxb628DBiTG3AqdW\nedZW4AgeVUmaAjdxzb0knyQ5OtG0yHDv+z6Goi8AHwLbJ8a8zlAE5uJnvcu/C8NI0mXjJi5NqKoH\ngONJvgRuAH4Zu84D66rK34ykNcM/JGlUVduAe5I8PjadA64dvy8BPyf5T/QtSbPiuZ0EVNV2YGuS\nlaraANwIvAPcxfAyk7uBiwu4mBgqaaas2Ka5N2aaHwMWWmsPAzuBr4GXgV2ttWWGzXxP7/33ccwO\n4H5gU2vtit776bH9Noaqb1taawutta96779OfVGSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEm6\nNP4G+hI3giVPkT4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f21943d3ed0>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Histogram of power consumption"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "THRESHOLD = 1\n",
      "appliance_filtered = (appliance[appliance > THRESHOLD]).icol(0).dropna()\n",
      "\n",
      "xlabel('power (watts)')\n",
      "ylabel('frequency')\n",
      "h = hist(appliance_filtered.values, bins=100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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QBYI5wIOZORwRA1TzF0bMoRqRGKt9gB3RA/Pmzaavr7d+3zbTiX0erRtqgO6ooxtqAOto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\nSZIkSZIkSZIkSZIkSZIkSZLazv8HTOtNTYI51KwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f21943d3710>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##### Conclusions from the histogram:\n",
      "\n",
      "* oven spends a lot of its time consuming about 2-50 Watts\n",
      "* appears to be properly 'on' when it's consuming over 1600 watts\n",
      "* So let's use 1000 watts as the on power threshold."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ON_POWER_THRESHOLD = 1000"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In NILMTK v0.2, on_power_threshold is stored as metadata per appliance.\n",
      "\n",
      "#### Number of hours the oven was switched on for"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hours_on = sstats.hours_on(appliance, on_power_threshold=ON_POWER_THRESHOLD)\n",
      "\n",
      "print(APPLIANCE_TYPE + ' was on for {:.1f} hours.'.format(hours_on))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "oven was on for 4.5 hours.\n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Total energy consumed by the oven"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kwh = sstats.energy(appliance)\n",
      "\n",
      "print(APPLIANCE_TYPE + ' consumed {:.1f} kWh.'.format(kwh))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "oven consumed 3.8 kWh.\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Explore usage behaviour of an appliance"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Usage (hours on and energy used) per period"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "usage = sstats.usage_per_period(appliance,\n",
      "                                freq='D', \n",
      "                                on_power_threshold=ON_POWER_THRESHOLD)\n",
      "usage.head(n=7)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>hours_on</th>\n",
        "      <th>kwh</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2011-04-18</th>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-04-19</th>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000063</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-04-20</th>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000010</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-04-21</th>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000014</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-04-22</th>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000004</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-04-23</th>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.007393</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-04-24</th>\n",
        "      <td> 0.308056</td>\n",
        "      <td> 0.510096</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>7 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "            hours_on       kwh\n",
        "2011-04-18       NaN       NaN\n",
        "2011-04-19  0.000000  0.000063\n",
        "2011-04-20  0.000000  0.000010\n",
        "2011-04-21  0.000000  0.000014\n",
        "2011-04-22  0.000000  0.000004\n",
        "2011-04-23  0.000000  0.007393\n",
        "2011-04-24  0.308056  0.510096\n",
        "\n",
        "[7 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "Sanity-check by looking at power data "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_series(appliance[:\"2011-04-24\"], \n",
      "            date_format='%Y-%m-%d', \n",
      "            tz_localize=False)\n",
      "title(APPLIANCE_TYPE + ' power demand')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "<matplotlib.text.Text at 0x7f2191b51390>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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sxBdDRMQy4HXAZ6hO770vsKAsyyt7Wtg0iogoiuLlVH2+uCiK35RleXOv65ou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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f2191f9d810>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Usage of the oven, hour-by-hour, over an average day"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dist = sstats.activity_distribution(appliance, \n",
      "                                    bin_size='H', \n",
      "                                    timespan='D', \n",
      "                                    on_power_threshold=ON_POWER_THRESHOLD)\n",
      "\n",
      "# Graph formatting\n",
      "x = np.arange(dist.size)\n",
      "ylabel('frequency')\n",
      "xlabel('hour of day')\n",
      "title('Usage of the oven, hour-by-hour, over an average day')\n",
      "xlim([0, 24])\n",
      "xticks(range(0, 25, 6))\n",
      "bar(x, dist.values)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "<Container object of 24 artists>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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WzHwX8HfAT2a3Kk1iF+BzrLnjviIz/zEz3wF8Dzh2VirTZMZ67U4Azq6fv3OA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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f2191aa8650>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Usage of the oven, day-by-day, over an average week"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dist = sstats.activity_distribution(appliance, \n",
      "                                    bin_size='D', \n",
      "                                    timespan='W', \n",
      "                                    on_power_threshold=ON_POWER_THRESHOLD)\n",
      "x = np.arange(dist.size)\n",
      "ylabel('frequency')\n",
      "xlabel('day of week')\n",
      "title('')\n",
      "xticks(np.arange(7)+0.5, calendar.day_name[0:7])\n",
      "bar(x, dist.values)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "<Container object of 7 artists>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f2191f07c90>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Histogram of on-durations"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get a Series of booleans indicating when the oven is on:\n",
      "on_series = sstats.on(appliance, on_power_threshold=ON_POWER_THRESHOLD)\n",
      "\n",
      "# Now get the length of every on-duration\n",
      "on_durations = sstats.durations(on_series, \n",
      "                                on_or_off='on',\n",
      "                                ignore_n_off_samples=10)\n",
      "xlabel('minutes on')\n",
      "ylabel('frequency')\n",
      "title('Distribution of on-durations for oven')\n",
      "h = hist(on_durations/60, bins=10)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Jdh0MA12SNCl5HYyx8Ry6JEkNYKBLktQAHTvkHhFvBQ4FFgP9mfmpTtUiSdK6\nriN76BGxMXAZcEpmngXsEhH7dqIWSZKaoFOH3HcHHsnMZ8rHdwEHdagWSZLWeZ065L4F0Nv2eGU5\nbVxsuOEG9C27d7yaq8Vzq/6bJ9fbqNNljGh173Kgq9NljOjJxxev1T3bV6yYyfLlq2qoqFnsp+rs\nq2qq9tOjjz7Ck48vnoCK1t5kq69Tgf5boLvt8WbltCF1dXVN7lRRRx15bacrkDRV7X7tWZ0u4Xmd\nOuR+D/DKiJhRPn4zcEOHapEkaZ3XsT3fcpT7u4AlwNOZeXanapEkSZIkSZIkSZIkSZIkNcOk/jmY\nl4etLiLuAVaXD5/NzP06Wc9kERFbAecAu2TmbuW0zYHzgAeBecCCzJxcPyjtgGH66pPA3m0v+3Rm\n3tqB8iaNiJgLnA38BJgDLMvMs92uXmyEfvokblMvEhFdwPUUvwCbAcwFjgU2Zgzb1KS9fWrb5WF/\nLzOfiYhvRcS+mfnPna5tkvpueRldvdgewHXAa9umnQvckpnfioiDgQuBoztR3CQzVF/1Z+b8DtUz\nWc0Cvp6ZCwEi4j8j4gbgeNyu2g3XT25TQ7srM88FiIjrgMOAvRjDNjVpA53hLw9roA9t54g4A9gI\nWJSZN3a6oMkgM78dEfsMmnwgxZ4DwN3AlRNa1CQ1TF8REQuAp4BpwKWZuXrwa6aSzPzRoEnrAU/g\ndvUiI/ST29QgmdlPsaNBREynOKJxP/AZxrBNTebbp9Z6edgGOj8zL6B48xdExFs6XdAk1r5trQRm\nRcRk/n+hk64BLs7Miyj67NIO1zOpRMShwE2ZeT9uV8Ma1E9uU8OIiP2BhcDCzPwxY9ymJvPGNqbL\nw051mbmo/LcPuBPwkNbwFvPCtrUpsKLsNw2Smfe17T3dBnhXxFJEzAf2zsyPlJPcroYwuJ/cpoaX\nmTdn5tuBHSLiRIptatPy6VG3qckc6F4etqKI2DEijm2bNA94oFP1rANuoNieoDhvfH0Ha5nUIuKC\ntoduV6WIOAjYPzNPiYhtImJ33K5eYqh+cpt6qYh4dUQc2DbpIWB7im1o93LaqNvUujDK3cvDjiIi\ntga+APyU4lvc9Mw8tbNVTQ4RsRfFIJK3UQyy/BzFOIPzgUcoRpOemZlLOlbkJDFMX/0VxUjbxcDO\nwMczc0p/AEfE64HbgUUUn6GbUPz/txC3q+cN009fBHbEbepFImIH4LMUvwhYH9gJ+HPgGdymJEmS\nJEmSJEkeX/86AAADFUlEQVSSJEmSJEmSJEmSJEmS9DJFxGsj4vZxaOf2iNh79FdKmiwm85XiJI3d\nvcD7xqGd/nFoQ9IEmsx3W5MERMQJwF8CXwdeQXF7078AXk9xX+nHgUMy8zmKuzOdBqwXEYdRXH3q\nNoqrdO0KXJKZX46Ioynu7vQe4EfA3wO/k5nzI+IoiktynhwR7yqXPa1seymwFXBHZl5R3ub4K8Bv\nKK7+9WRmnj7EOuxa1vww8EqKmwn9NCLOAk6iuILYa4FXAcdk5r+OV/9JU4V76NIkl5mXATcDrcw8\nAjiB4jaKl2fmnhSX+51fvvaMtvmuBa4Cts/MPwYOAT5RPncV8Ivy7yeBS9rm+7vyuUsy86TMfBz4\nPPD9zFwAfAj4eETMAw4AZmXm6Zn5ZxSB/yIRsT7wHeCczDyzrOE7ETEjMz8B/BvwbGYeAlwMnDIe\n/SZNNe6hS+uOu8t/HwJ6265//Utg6xHmu6f890Fgy2FeM9R9HdqnvR1YPyL2LB8/TLGnvQi4KCKu\nA75B2xeDNjsBszPzXoDMvD8iNgFeQ3Htanhh3R4E3j3Cukgahnvo0rrjqfLf/ra/Bx6PdKOlpwHK\nQ/Ltr+vnhc+A9YeYr3/Q3xdl5gmZeQJFwN+emb+iODz/VeBIYFFETBuhneEMrM9z+LkkrRX/x5HW\nDV0MH9qjPTecx4Btyr93HfTcGmB6RPyviHgNcBPFXdgG/AOwTXl7zL0y84bMfGfZ3iaD2rofWBIR\nuwBExE7AKooBfFXqlFTB4G/SkiaZiDgYOAbYrtVq/YDiHPSurVbriVar1QKOLZ/7YavVOhPYvdVq\nbdxqtVZRDDh7VavV+nGr1ToWmN9qtbqWLVv2/Var9Wvg461W69XADGDfVqv1+LJly37aarU2oDhX\n/hbgGop7fb+/1WrNb7VaBwO3ZOadrVZrFrCg1Wq9rtVqHQF8JzNvb69/2bJlfa1W63bgL1ut1puA\ndwInZeavy8F5RwBzWq3WHcCngJ1brdZvly1b1h74kiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJ\nU8v/B/J3lgq89ZHNAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f2191e46ad0>"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Descriptive stats of whole dataset\n",
      "\n",
      "```python\n",
      "redd.describe()\n",
      "```\n",
      "```\n",
      "NUMBER OF BUILDINGS: 6\n",
      "\n",
      "NUMBER OF APPLIANCES PER BUILDING:\n",
      "  min  =  9.00\n",
      "  mean = 16.50\n",
      "  mode = 15.00\n",
      "  max  = 23.00\n",
      "  std  =  4.31\n",
      "PROPORTION OF ENERGY SUBMETERED PER BUILDLING:\n",
      "  min  =  0.58\n",
      "  mean =  0.70\n",
      "  mode =  0.58\n",
      "  max  =  0.89\n",
      "  std  =  0.11\n",
      "DROPOUT RATE PER CHANNEL, INCLUDING LARGE GAPS:\n",
      "  min  =  0.13\n",
      "  mean =  0.58\n",
      "  mode =  0.92\n",
      "  max  =  0.92\n",
      "  std  =  0.24\n",
      "DROPOUT RATE PER CHANNEL, IGNORING LARGE GAPS:\n",
      "  min  =  0.00\n",
      "  mean =  0.10\n",
      "  mode =  0.09\n",
      "  max  =  0.17\n",
      "  std  =  0.04\n",
      "MAINS UPTIME PER BUILDING (DAYS):\n",
      "  min  =  3.60\n",
      "  mean = 15.12\n",
      "  mode =  3.60\n",
      "  max  = 19.44\n",
      "  std  =  5.43\n",
      "PROPORTION OF TIME SLICES WHERE > 70% ENERGY IS SUBMETERED:\n",
      "  min  =  0.23\n",
      "  mean =  0.64\n",
      "  mode =  0.23\n",
      "  max  =  0.98\n",
      "  std  =  0.26\n",
      "  ```"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Preprocessing"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First, for reference, here's the plot from above showing missing samples:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "building = redd.buildings[1]\n",
      "bstats.plot_missing_samples_using_rectangles(building.utility.electric)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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0mpSo+3fA7ol6MhF6bj4MfACYFHqfYrqBR7PKdBzHcZyJRtv14JjZOkmPS+o1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\nSDpX0rnAY0BPQp6FvxsSZYrreyjIXQ/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Ag4SxMieY2XmSTgXmS3o8Lj+P0Gtzv6TvAA8Q\nxg+dENctJjTCNhBuZ22xv6QZwJuAPSX92cyWFuRnHKEXaWczGypYtxPwwVhWp0v6kZk9GFfPIgxA\nds4551wFzQzVMI/wA3+SmW1IF5rZR0ts/4SZXZRdEH/M35xZ9OXMui8UJmBmKwljfEoys+8C380s\n2jWz7gbghiK7HZl5/dXM9gvZfLBwsf1XAYeWydJ7gR8VNm5i+suBL8Z/m8RepS4z+0WZdJ1zzjkX\nNe0xcTO7BziH0reqgE1PXB0L7CXp/zTr+G3kj2b28Rr3ealMQ9E555xzBZoabNPMnq5imzWE2zxb\nJTN7rI59mjVQ2TnnnNsqNGMwrBsjVq16vqEJFhsJWz9a6fvTEyOj1eVaT91I9xkLSuW71eU6VjV6\nLfByHTmjXbaN1IV031mz9ivalvEGTgdptIHTDvzCNjK8XEeGl+vI8HIdOe1YtlOnTiralmnqLSrX\n3tqhB8d1pk7twXHF+bXApZrRg1OKN3A6UH//zQDMnn1YxW2XLXsIgL6+GWlXH7fdtpS+vhk1pVPo\nmmuuAmDu3OM2LStMvxZpPqdMKTZRdXmVzmPZsodYsWI506fvVHO+6lWsfFqlv/9menq6mDnzoIbS\ngPrqCgzXjYULr686jSVL+pkz5z11Ha/ZSuXbzFi0aDHTpk2ru2xS2e/qaGv084Xy1xrXnirVycL1\nxepRI3Uh3beUZgbbdM4555wbE7yB45xzzrmOU3UDR5I3hlrEy94555yrTcUfTkldki4FeiXtLGmB\npImSroxRwQu3f4ukSzLvT6mQfsm0WkHSv0jqia/fKekRSdMr7PPuGGah3Db9VRx7nKR5kp6RtEdm\n1SGSTq7qBJxzzjlXVQ/OfEIMqWfTBXGyvquKbWxmvzWz0zOLPlUu8XJptcjxwA4AZvZ/geVV7HMU\nsHMTjr0PcDuwWZhkM/stsLukxkYpOuecc1uJsk9RxbAKR5hZOvPwc8Cv4utxme1uJASQ/AbwAWBf\nM5st6aPADpLOJfxwLwa+BqwGJgC9ZvavMa1psadoH+CHMYYUkj4PbEuI5D1kZpfEaOIXAlcQglzu\nChxZJHjlNOB8wIAZwPeApXG/jWZ2QszjWWa2i6S3ERoqp0h6wMyuKEjvaOBw4JF43E/H/L4eOF7S\ngcBlwOXA/cBrgSVmdh2bx7AqyszuiscptvqXwMnAzZXScc4557Z2lR4T7yNE9QbAzJ4Dro1vNwJI\neh/wczO7Mr5fRWhIYGbflnSWmZ0f180DXmZmF8T3J2SO9QozO1VSAvQD35V0OHCAmR0et++XdJOZ\nLZB0HHBrjAr+n8BbgesL8v9V4Cdmdr2kqcBSM3ttvB12fDaP8fVvJD0GXGpmK7IJSZpMaLjsYmYv\nxEbbPDObL+kuYIGZLZH0ekKj5zRgDbB3TPtbFcq6kqeAPcttMHlyF+PHb0tPTxcQJmyqZHBw4qZt\n09e9vROZMqW7pnQKdXdP2GLfwvRrke5bT34qncfg4ETy+a668lWvYuXTKo18zs1KI/18e3q6qk4j\nPeZYUCrfg4Mr6e6eUNN5lZL9ro62ZtSRcteaeoyF706nqrZsK9XJwvXF6lGzfheKqdTA2Q54scz6\nNwLTCb0LqXKzI+8FPJy+MbN03M3GdLmZ5SSlZ7k30CXpzPj+cWBKJj2L/68GipXMXsBFMd1Vknpi\nA6qeGZx3i/k8Jfaw9ALZHqNx8Th3Sfom8FPgb8C/1XGsYtYD25fbYHAw3NnK58P/1cxGOTCwZtO2\n6euBgTWsXj1UUzqFhobWbbFvYfq1SPetJz+VzmNgYA35/Nq68lWvYuXTKvn8Wnp6uhrKSyN1BYY/\n33x+bdVppMccC8rle2hoXU3nVUr2uzraGv18ofy1plbtONtuu6ilbCvVycL1xepRs34Xiqk0Budx\nSkcHHwfcCrwLOE7SG0pstwFA0j7A3YReIeKyeZm0ioUZuAtYZWYXmdlFwALgwRJ5KeZuQsMESTsC\ng2aWIzRMJsXlhQOIXwK2kbSnpHRaxXGEBtg64JKYl8uAOzL7jJPUJ+l1wO1mdijwn8B5JfJWSeE5\n9QIrim3onHPOuc2VbeCY2UrgyTiWZRNJLweOBV5HuG2yBLhW0jGEcSLT4+0lgKWSLgSOAK4E1ks6\nV9IXgMFMWntJ2k/S8cAkSUeb2WLgTkkXxttI7wCekvRWwhiYEyTtArwJOFLSKwtO4TTgiLjvBcD7\n4/I/ERoxZwNvjsebE9f9GvgsYXzN4fE488xsEDgVmB97lE5juAdpMSFC+rmxTM+WdEbM7xa3piTd\nU/CUVLp8h5inScBHJB2QWT2L0CvknHPOuQqqCdUwDzhP0klmtgHAzP5G+EFP/ZHhp6WuBdJbSpjZ\nMQXpnVrkGNm0lhLH8MT9Lyiy/WLCwOLUocUyHhtoHyqyfANwdGbR1Zl1XynYfNfMuhuAG4qkt5DN\nBxF/oFh+AOJA5EfN7L4i6TwHfDH+y+4zHdgfaP28/s4551wbqPiYuJndA5xD6VtVrjZPALUG0OkC\njjWzcuOhnHPOORdVFWzTzJ4e6YxsLczsiTr2eWAk8uKcc851qnqeJnJj1KpVzxcbqF21RsLWj1b6\n/vTEyGh1udZTN9J9xoJS+W51uY5VjV4LvFxHzmiXbSN1Id131qz9irZlvIHTQRpt4LQDv7CNDC/X\nkeHlOjK8XEdOO5bt1KmTirZlqrpF5bYO7dCD4zpTp/bguOL8WuBSzejBKcUbOB2ovz9Ec5g9u3Lo\nqmXLHgKgr29G2tXHbbctpa9vRk3pFLrmmhBebO7c4Qe/CtOvRZrPKVNm1pyXSuexbNlDrFixnOnT\nd6o5X/UqVj6t0t9/Mz09XcyceVBDaUB9dQWG68bChddXncaSJf3MmVPreP2RUSrfZsaiRYuZNm1a\n3WWTyn5XR1ujny+Uv9a49lSpThauL1aPGqkL6b6lVBNs0znnnHOurbR1A0dSW+e/FlvTuTrnnHON\nassfTUldMfJ4r6SdJS2QNFHSlTGQJpJ2l/SjCun8naTrYuDMYutPybyeJOl3Dea7v4ptroozN39O\n0s8yszOfLOmQRo7vnHPObS3asoEDzAeuM7Nn0wVmtga4KvP+QTMrOaNw3OYJisxMnPGpzLbPm9kh\ndee4eg+b2b+Z2ReA5cBH4/EvB84tEo7COeeccwXabpCxpInAEWaWhnd4DvhVfD0us90ngVPNbJf4\nfj7hfJ8kxMUy4JNxnz3j+pnAhWa2SNJHgR1i787twKuAy8xssqR3AV8DbiREMd8D+GczWx5jY11G\nCBQ6BPw78HEzu5rNwzkUZWafz7ztA7K9PrcSwkB8vXJJOeecc1uvduzB6QMG0jdm9pyZXVu4kZnN\nT19Leiewm5l9LMa22gAsMLP0Yf+/mdknCT02J8X9vw08Z2bnm9lNZnYVoTGFmd0I/B6438w+AvwM\n+KeY1sXAD8zsHOB6QgTzq+N+WwTeLEbSGyRdS2iM3ZhZ9SQhuKlzzjnnymi7HhxgO6BUTKZSE93t\nCTyUef8Iw709G4GH4+scoUemWmk08dWEqOMQenPSYz1aQ1rDiZr9EThG0vnAlxgOXvoisH2p/SZP\n7mL8+G3p6ekCwoRNlQwOTty0bfq6t3ciU6Z015ROoe7uCVvsW5h+LdJ968lPpfMYHJxIPt9VV77q\nVax8WqWRz7lZaaSfb09PV9VppMccC0rle3BwJd3dE2o6r1Ky39XR1ow6Uu5aU4+x8N3pVNWWbaU6\nWbi+WD1q1u9CMe3YwHmc0oE/S83M/L/A7Mz7XRluDI2jdMNoA4CkvWPQ0VLGZY59H7A74RbVriX3\nKCKOr3mnmX0/LnoMeEdmk17CuJyiBgfXApDPh/+rmY1yYGDNpm3T1wMDa1i9eqimdAoNDa3bYt/C\n9GuR7ltPfiqdx8DAGvL5tXXlq17FyqdV8vm19PR0NZSXRuoKDH+++fzaqtNIjzkWlMv30NC6ms6r\nlOx3dbQ1+vlC+WtNrdpxtt12UUvZVqqTheuL1aNm/S4U03YNHDNbKelJSdPM7Kl0eRybcyywl6RZ\nwL7AJElzzGyhpHdI+jaht+YFYKOkqcCRhLE2fcAHgemSZptZP7BU0oXAGkmvA3ri2Jw/AXsDx0p6\nsCCNM4GvS9qL0BjbovEkqRd40MymFKx6EXiXpJ3j630JY3hSBwDn1196zjnn3Nah7Ro40TzgPEkn\nmdkG2PQU1YmZbW4DvpF5/z0z+x8ASW8DHjWzVcB7M9ucF/8R0zym4Lg/zLzOTp+4KQ1JuxEGHOcl\nTWd4bE7Wh4CLChea2XMF+dlE0luB+8zs7mLrnXPOOTesLRs4ZnaPpHMIt2yerbR9dL6kJYQxNr+I\nj4iPhJ2Az0m6F9gN+HSRba4zs1rH5zxtZmc3nDvnnHNuK9CWDRwAM3u6xu3fOVJ5KTjOLcAtFbap\nefCxmd1bd6acc865rUypQbmuDa1a9XypwdJVaYdo4j64cGS0ulw7NZp4q8t1rGr0WuDlOnJGu2yb\nEU181qz9irZlvIHTQRpt4LQDv7CNDC/XkeHlOjK8XEdOO5bt1KmTirZl2nGiP+ecc865stp2DI5r\nvna4ReU6U6feonLF+bXApZpxi6oUb+B0oP7+mwGYPfuwitsuWxYmXe7rm5Hey+S225bS1zejpnQK\nXXNNiHs6d+5xm5YVpl+LNJ9TpsysOS+VzmPZsodYsWI506fvVHO+6lWsfFqlv/9menq6mDnzoIbS\ngPrqCgzXjYULr686jSVL+pkz5z11Ha/ZSuXbzFi0aDHTpk2ru2xS2e/qaGv084Xy1xrXnirVycL1\nxepRI3Uh3bcUv0XlnHPOuY7jDRznnHPOdZyqGziSvDHUIl72zjnnXG0qjsGR1AVcAFwQ4z2dC3wC\nuAzYxsxOKNj+LcA7zOz0+P4UM/uPMulPLJVWK0j6F+DnMdTCO4GvA4eY2Yoy+7wbuMvMSgbClNRv\nZrNLrY/bXAr8BVgD7AOcYmbPAIdI2t3MvlnHKTnnnHNbnWp6BuYTQgtsCokQ4z5dVWxjM/tt2riJ\nPlUu8XJptcjxwA4AZvZ/KRO9O+MoYOcmHHuNmZ1tZl8mBPT895iP3wK7S2pslKJzzjm3lSjbgxN7\nV44wszSI5XPAr+LrcZntbiTEhPoG8AFgXzObHSNv7yDpXOB2YDHwNWA1MAHoNbN/jWlNiz0Y+wA/\nNLPvxrQ/D2wLvAQMmdklkk4ALgSuIMR+2hU40sw2m51I0jRC9G0DZgDfA5bG/Taa2Qkxj2eZ2S4x\nCOfOwCmSHjCzKwrSOxo4HHgkHvfTMb+vB46XdCChN+py4H7gtcASM7sOWFiurAHM7HOZt9sC2fP5\nJXAycHOp/SdP7mL8+G3p6ekCwoRNlQwOTty0bfq6t3ciU6Z015ROoe7uCVvsW5h+LdJ968lPpfMY\nHJxIPt9VV77qVax8WqWRz7lZaaSfb09PV9VppMccC0rle3BwJd3dE2o6r1Ky39XR1ow6Uu5aU4+x\n8N3pVNWWbaU6Wbi+WD1q1u9CMZVuUfUBA+mbGO362vh2I4Ck9xFu6VwZ368iNCQws29LOsvMzo/r\n5gEvM7ML4vvsLalXmNmpkhKgH/iupMOBA8zs8Lh9v6SbzGyBpOOAW83sPEn/CbwVuL4g/18FfmJm\n10uaCiw1s9dKWkDoqdmUx/j6N5IeAy4tvCUlaTKh4bKLmb0QG23zzGy+pLuABWa2RNLrCY2e0wi3\nmvaOaX+rQllnj7VDPJ/sM7BPAXuW229wcC0A+Xz4v5rZKAcG1mzaNn09MLCG1auHakqn0NDQui32\nLUy/Fum+9eSn0nkMDKwhn19bV77qVax8WiWfX0tPT1dDeWmkrsDw55vPr606jfSYY0G5fA8Nravp\nvErJfldHW6OfL5S/1tSqHWfbbRe1lG2lOlm4vlg9atbvQjGVblFtB7xYZv0bgZOASZll5cI/7AU8\nnL4xswXx5cZ0uZnlCBG/ITQOuiSdKelM4HFgSiY9i/+vzuxTeLxHYrqrgJ7YgKonRMVuMZ+nxLz0\nsnkDcVw8zl3AN4GfAjcAG2o5iKQe4D+BE2KDMrUe2L6OfDvnnHNbnUoNnMcJP+TFjANuBd4FHCfp\nDSW22wAgaR/gbkKvEHHZvExaxeIo3QWsMrOLzOwiYAHwYIm8FHM3oWGCpB2BwdiAGiI2yiRNL9jn\nJWAbSXtKSqdVHEdogK0DLol5uQy4I7PPOEl9kl4H3G5mhxIaKueVyNsWJL2S0Et0hpktl/RPmdW9\nQMmBzs4555wbVraBY2YrgSfjWJZNJL0cOBZ4HeG2yRLgWknHEMaJTI+3lwCWSroQOAK4Elgv6VxJ\nXwAGM2ntJWk/SccDkyQdbWaLgTslXRhvI70DeErSWwljYE6QtAvwJuDI2EDIOg04Iu57AfD+uPxP\nhEbM2cCb4/HmxHW/Bj5LGF9zeDzOPDMbBE4F5scenNMY7kFaDJxIeMJsG+BsSWfE/G5xa0rSPZL2\nKFLkN8UyvUZSP5Cd5nYWoVfIOeeccxVUE6phHnCepJPMbAOAmf2N8IOe+iPDT0tdC5yZrjCzYwrS\nO7XIMbJpLSWO4Yn7X1Bk+8WEgcWpQ4tlPDbQPlRk+Qbg6MyiqzPrvlKw+a6ZdTcQbjsVpreQzQcR\nf6BYfgDiQORHzey+IukUnXM69jLtz+YNHuecc86VUPExcTO7BziH0reqXG2eYPPBw9XoAo41s3Lj\noZxzzjkX1TPY1o1Rq1Y9X2wcU9XaIZq4Pz0xMlpdrp0aTbzV5TpWNXot8HIdOaNdts2IJj5r1n5F\n2zLewOkgjTZw2oFf2EaGl+vI8HIdGV6uI6cdy3bq1ElF2zIe48g555xzHaeaQcZuK9EOt6hcZ+rU\nW1SuOL8WuFQzblGV4g2cDtTfH6I5zJ5dOXTVsmUPAdDXNyO9l8ltty2lr29GTekUuuaaEF5s7tzh\nB78K069Fms8pU2bWnJdK57Fs2UOsWLGc6dN3qjlf9SpWPq3S338zPT1dzJx5UENpQH11BYbrxsKF\n11edxpIl/cyZU+t4/ZFRKt9mxqJFi5k2bVrdZZPKfldHW6OfL5S/1rj2VKlOFq4vVo8aqQvpvqX4\nLSrnnHPOdZymNXAkeWNphHjZOuecc7Vp+IdTUleMAt4raecYyLKpJL1R0lJJBzc77Zj+xyQ9OhJp\nZ47xPUk7VbHdHEkPS3pnZvFekv59BLPnnHPOdZRm9AzMB64zs2cpHk+qYWb2e+CekUg7pv+NkUo7\nYyMVykfSzsAzhBhgm7Y1s7uBtZI+PJIZdM455zpFQ4OMJU0EjjCzNNRCHviVpNcQQjasIoRKeDew\nv5l9TNK5wH7APxHCG/wR+DvgVjP7oaSpwCWEBs3uwPfN7NaY/j9KmgvsAcyNASlfA1wI/JkQWPMK\nM7tT0kJgF+C/gIOAa83sskzedyEEzLwTeCqz/ISY3rcIgUH/DyHQ5nbAB4HXxv0+A+SAM4B7gb8n\nxLsaBH4ck3sAeAvwOeBXwPPlytPMHgMei2VU6MZYXleWS8M555xzjT9F1QcMpG/M7DlCw4YYTPMD\nZjYoaSYh6OU2wLPAKWa2XtL3zOyGGLX7PuCHhMZIL/BNYAKQZI53n5ldKekzhAbS14CvAL8wsx/H\nHpDrgZmEhsethMCZ2wOvLsj7xcAPzOwnkvqAs+I5LJB0HPAHMzs/5j0P/MzMHo/n8FMzWyLpNuBU\nM7td0iHAV83sPZK+BFxsZqdIehWwrZk9RWOeIjTsSpo8uYvx47elp6cLCBM2VTI4OHHTtunr3t6J\nTJnSXVM6hbq7J2yxb2H6tUj3rSc/lc5jcHAi+XxXXfmqV7HyaZVGPudmpZF+vj09XVWnkR5zLCiV\n78HBlXR3T6jpvErJfldHWzPqSLlrTT3GwnenU1VbtpXqZOH6YvWoWb8LxTTawNkOKBUf6Rbg8tjD\n8jhwB6E3Y4aZXS5pPLBnbED8FZgS97uR0HC6CVhNiOqdejj+/ywhyjfAXsAzcXzLOMItHuLrh83s\nJWAN8FBB/vbILCs2/uZ+ADO7E0DSckmHAgcTenDSY78tjg3aHkinfxyX2f8ZmmM98DJJ26RBTwsN\nDq4FIJ8P/1czG+XAwJpN26avBwbWsHr1UE3pFBoaWrfFvoXp1yLdt578VDqPgYE15PNr68pXvYqV\nT6vk82vp6elqKC+N1BUY/nzz+bVVp5Eecywol++hoXU1nVcp2e/qaGv084Xy15pateNsu+2ilrKt\nVCcL1xerR836XSim0TE4j1MiCKeZrSc0cr4D/JRwe+V04LG4yTuBw8zsPDO7CEivVnsBPzKzNwE3\nMxx9PDsV87jM+7uBW8zsIjP7MqEXKFVuzMt9hFtgsHlk8lLmE3uDzCyXOfbPYv4vBBZVeexqFE49\n3QusLNW4cc4559ywhho4ZrYSeFLStBKbLAQSM3uU8ON/APCTuO6/gW0kzZd0BtAl6XhgInBhXLYv\ncJWkNxAaPsdK2hE4EnhTvLV0GmFszmclXcxwL8qJwPSYZjFnAsdJupAQ3XuSpA9IOozQO/SvpFva\nXQAAEjdJREFUkl6ZOdfFwDTgqkwaHwY+KelMwi2vxyW9HDiW8OTT0cUOLOkGSUeUWHd2PP4xkt6W\nWTWL0FB0zjnnXAXNmMl4HnCepJMKexfM7BZCowYz+yuwQ2bdamB2ZvOLM69/X+Q42ekK31uw7iOF\nG5vZ2cDZpTJtZo8QepGKHX+zHp3YaFkP/M7M7s2k8UCxYxMaV0XFRtlEYHGJfH0R+GLBPpOB9xPK\n2jnnnHMVNPyYuJndA5xDiVtVHWIB8B8MPx3ViCHCk2fra9hnMnC8meWbcHznnHOu4zUlFpWZPd2M\ndMYqM5vbxLRW1bHP2Igo6JxzzrWJwoGsro2tWvV8QwOb2yGauD89MTJaXa6dGk281eU6VjV6LfBy\nHTmjXbbNiCY+a9Z+Rdsy3sDpII02cNqBX9hGhpfryPByHRleriOnHct26tRJRdsyTblF5TpDO/Tg\nuM7UqT04rji/FrhUM3pwSvEGTgfq778ZgNmzD6u47bJlYa7Dvr4ZaVcft922lL6+GTWlU+iaa8LT\n9HPnHrdpWWH6tUjzOWXKzJrzUuk8li17iBUrljN9+k4156texcqnVfr7b6anp4uZMw9qKA2or67A\ncN1YuPD6qtNYsqSfOXPeU9fxmq1Uvs2MRYsWM23atLrLJpX9ro62Rj9fKH+tce2pUp0sXF+sHjVS\nF9J9S2lGsE3nnHPOuTHFGzjOOeec6zgd3cCJgTHbXqech3POOTdaOvKHU1KXpEuBXkk7S1qQWfd9\nSWdIur7Evv+fpH2KLL9YUn8Deaq4r6RxkuZJekZSNnL4IZJOrvfYzjnn3NamIxs4hMCY15nZs2SC\nXsbI5jub2cXAMcV2NLMPmdndRVZ9Y0Ryurl9gNsZDjya5um3wO4xTpZzzjnnKui4p6gkTSSEQkjj\nQeUZjvL9GUIAznOB/hho8yngaeBdwHHAF4AFZvZ9STOB84A7CLGo0mNsB1wJPAM8R4gT9SszOy0G\n2DwceIQQNPPTZvYCIfBoWWZ2V0y/2OpfAicTIqw755xzroyOa+AAfcBA+sbMnmM4gvllwL5mdj6A\npO8A7zCzT8ZbWgNAP8O9PlcAHzOzP0o6FHh7XH4ikDezz0gaF9/Pj0ExLwd2MbMXYkNqHjDfzL7V\n4Hk9BexZboPJk7sYP35benq6gDBhUyWDgxM3bZu+7u2dyJQp3TWlU6i7e8IW+xamX4t033ryU+k8\nBgcnks931ZWvehUrn1Zp5HNuVhrp59vT01V1Gukxx4JS+R4cXEl394SazquU7Hd1tDWjjpS71tRj\nLHx3OlW1ZVupThauL1aPmvW7UEwnNnC2A14ssa7YbIcPAJjZo7BF78kewEPx9aMFyx+O+22U9Fhc\nvhuhcXRKTKeXEFyzGdYD25fbYHAw3NnK58P/1cxGOTCwZtO26euBgTWsXj1UUzqFhobWbbFvYfq1\nSPetJz+VzmNgYA35/Nq68lWvYuXTKvn8Wnp6uhrKSyN1BYY/33x+bdVppMccC8rle2hoXU3nVUr2\nuzraGv18ofy1plbtONtuu6ilbCvVycL1xepRs34XiunEBs7jVB/ZfByZMTqZZWlD6D5gd+APwK6Z\nbe4DXgebnnDaOS5/GFgHXGJmGyTtCkyrMf/ZfGT1AivqTMs555zbqnTcIGMzWwk8KalYw+JjhDE4\nn5Q0FTgSeJOktwBI2hN4E3Bk3P8k4FxJnwcOjfu+A/guMEnSfxDG9eSAjWY2CJxKuF11JnAawz1A\nm0i6p+ApqXT5DpLOBiYBH5F0QGb1LOCn9ZSJc845t7XpxB4cCONezpN0kpltSBea2RnAGZnt3pvd\nycz+l9CQST0FHJF5fxaApFcAXzCzB+L744CVMY0bgBtKZUzSgcCjZnZf4bo4XuiL8V92n+nA/oRB\n0M4555yroCMbOGZ2j6RzCLd1nh2BQ2wPXCTp98COhMZOqXE/hZ4Aag2g0wUcm22sOeecc660jmzg\nAJjZ0yOY9rPAu+vc94k69nmgnmM555xzW6tiTxW5NrVq1fOFA6Zr0kjY+tFK35+eGBmtLtd66ka6\nz1hQKt+tLtexqtFrgZfryBntsm2kLqT7zpq1n7dlnHPOOeecc84555xzzjnnnHPOOeecc84555xz\nzjnnnHPOOeecc84555xzzjnnnHPOOee2apJ8hkrXNry+unbjdda5USTpXZJmtjofnSaW676tzken\n8fo6ciRNiv/7j3ATbS11tvkBh5xrgKR3AZcAa5Mk+XMul1snaVwul2t11tpaplz/mpZrq/PUCby+\njgxJOyVJ8gNgcpIkd5rZS16uzbE11dltWp0B5wqsBn4A7AocCGBmDQURdUCRcnVN4fV1ZEwB/grs\nBOwHXq5NtNXUWW/guJaRNF7SnunruPhPwIXAOuDNknaO672uVqnGcvWu/yp5fR15mXJdCVwK9AIH\nS5oc13t9rcHWXmc77oRce5DUA/wEuAPAzF6Mq9bHvyauI/yFkY4Z6ci/MJqtinL9KZuXq6uC19eR\nI+l9kq6Fzcp1pZndDiwC9gYOiuu9XKvkddbH4LhRJmlGkiRrgVcAtwOzkyTZKZfL3SJpWzPbAJDL\n5R5NkmQG8PYkSfYCVuZyuWdbmPUxrYZyfaygXJ/O5XKrW5j1Mc3r68iKvQpvAk5PkuThXC53r6Rt\ngY25XI4kSYzQuJmcJMmLSZJszOVyz7c002Oc19lh3oPjRts5wPsI99cfAD4MnC5pWjqQMNOVOg6Y\nDPSb2f2tyW7bqLdc72tNdtuG19cRIOnjkt4OdAO/AE4A/gPAzF6K22xrZn8DbgaOjdtMaE2O24rX\nWedGk6SXx//fLOlL2Xvpkq6XdEN8vU1m+etHP6ftxcu1+SR1Z157uY4ASe+X9J2CZfdKuqjItsdL\n+ujo5a79eJ0tzntw3IiR9EZJtwDEv8Qg/MXwhJltlLRdXPY+4K2SPgHsL6kr7nPXqGe6DXi5jgxJ\n75X0I8KYj5SXaxNIOkzSrMyi3wOPSOrJ9CZ8GPiUpMmSJknaPi6/2sy+PaoZbhNeZ8vzBo4bSX3A\nbEnzMsueAd4du59fkDQ+dknfA7yHMLhwbSsy20a8XJtI0o6SrgOOBj5vZrdmVq/Gy7VukmbGH+AP\nAU9nViVAn5nlzezFWL53AH8A7gfeShz0mhkc6yKvs9XxBo5rujhIEMKP7inEe+uSxpnZA8AfgcPi\nNhsk7Qp8wsxmm9nyUc9wm/ByHTEzCD0KJwI7Svo3SadL2jGOUboHL9eaxceTPwf8xMz+Gdhe0isB\nzOxuoFvSO+Lm4yRNA5YAZ5nZdWbmk1GW5nW2CuMrb+JcZZLeBkwEbjaz9CmHOWZ2gqT3SPoNcKWk\n/waeJfxIE0f0PxL/uQJeriMjlusk4JeEhuHfA+cTfjQWxtdvBw4lzMni5VolSa8ys2eAAeAG4HlJ\nnyb8QT1L0sNmdmZcNwFCL42kQTP7XMsyPsZ5na2dPybuGiJpSpIkXyZMGnUAcHCSJPlcLvdEkiS7\nx8c8/w74Z+BiM3swSZKjgMm5XO6OFmZ9TPNyHRkF5bo/4a/cR4ClwDNmdksulxtMkuQXwJeTJLma\n8Jjyjrlc7g8ty3gbkDQrSZIPAHOSJNkeuDuuOhD4sZndlCTJA8APkiT5JqFej8/lcnfGXsj1Lcr6\nmOZ1tn7ewHF1kTQ9SZK/AnsAk8zs8iRJ7id0nR6VJMnPgU8ArzGz85Ik+QfCl+8+wmRzf83lck+0\n7ATGKC/XkVGhXP/ZzL6XLbckSXYFXkmYKO0u4AUv19IUJpX7BPAtQk/i4cABZvaDJEnuN7NVALlc\n7plYtncBy4EDc7nc7zoxDlKjvM42zm9RuXqdCdwLPEyYn+JrZvaYpJ8T/jI7Cvi4ma2KY0fuA241\ns5/H/Z8ulqjzch0hpcr1Z8AbJB0N3ArMA54EpgK/iYMzB4HbWpPtsU3Sh4C/AAbMMLMnJT0DrAEu\nlHSwmf2XpH3N7E+SjiE85fO0meUkPdTC7I91Xmcb5IOMXb1+QJgpcykwGC90AE8R7g/3pH+1EerZ\nl8zs8tHPZnvIDCD2cm0SSW/IvC1Xrv8D9MZyfZTw9M6lZnbdaOa3nWTq62OEmYgfBHaWdEh86ukB\nwgR9b47zsRwb573ZBviImeUAzOyvo575Ma7Ka4HX2Sp4A8dVJGkPSZdJ+pfYFQ0h2u92ZjYIfB84\nRdJ2ZjZAqFebplM3s/Xp7KRumKR/iE8/7A+kc35Mxcu1IXGeoF8Bt6RP7RAeSy5Xruljsz8yswVm\n9sLo53zsSyeGy9S71cAy4EXgWkKvA2b2LOFW1fMW4h59Dfh3M/uxl+2WJO0nqRc2K9tevM42xBs4\nriRJ3ZLOIsxJ8Yf4/6fi6v8HvF3SK83sSsKtkvmSziAMhvtzK/I81sW/ZpF0MvBuwo/D8UDaC/N7\nvFzrIunVkr4FfBa4BPg08Kq4+lbKl+vdsNmPiysg6a3AnfE2U2otcFiccPLrQK+k02IvRA+h4YOZ\nPZnpeXQZkmYD/wV8vGDVf+N1tiHewHFFKcwuuiuwg5ldZmY/BP6XeF/XzJ4DfgXsFXc5Fvgq8Gcz\n+7J5jKMtxMbN1Pi2G7jJzBYS5gr5J0lHxL/Ofo2Xa01i2c4GxpnZkWb2W8LsrkOwqb7eiJdrzTK3\nTNYT6uZ56TozWwaYpPfE204nEmIbXQ7c7bdPS1OcTZjwSPcpwBxJfx/XbeN1tnE+yNhtRmE69fcT\nGr9XALfE5f8AHAlMknSUmf0r4bbKUNx1o5kZYbChKxBvQ30QmCrpJ8DbCOMU/hQHW54LfAlYRJgb\nxMu1CrFX4SDCXEE/JQzKTC0nlPN3FWJ2TWL4Fp+XawWS/sE2D8C4N6F37EuSzgO+QXgS9y7gOQAz\nuxe4V9LLbTiMiMuI19hjCBMfXgb8zcy+K+kw4DRJJ5vZeoUwC15nG+A9OA7YdDvqK4SL2PnAjsA8\nM/tN3OQFwnTrXwKOknQwsAJ4L3g3aSmSJkj6BvBGQpTfx4B9CH+ZXRK3GUf4i/dpSTsSxjV4uVYg\naW/gEOBSwqRyxxP/2o1luoL4YxB/bJ8lxOTxci1D0nbxWnCTpF0zZfUXwi3Sa4HPAB80s5WEa8XB\ncd9xsFmMNBcVXGO/QBhj8/HYCwZwFmGOmwMA4piaZwiNIa+zdfAGjkttIIzOvyXeJjkf2DXTPf2o\nmT0Yt7uR8INyLfBzZaLWus1ZmG7+EUJ3/SBhFtJuM7sMGB/H4kAYBPuAmT1NGFD4My/Xit5GmB/k\necL8K/cA/yhppziwtYcwbiy1AC/XamwD/Am4ntBjk3oLcDKht+wm4BBJOwFXA9vEAbAbRzuzbaTw\nGvsFwpNn28aJDh8FvkN4vP5f4u2qq/A6Wzef6M8BkCTJi8DjZvZYfP9qYBszu1XS7sD7kiR5LWEq\n8IfN7NdJkvzNzJ7wSbrKS5LkDjNbJmk6YeDrS0mSHEpoJE4ijB3ZA/jfXC53T5Ik671cK0uS5AVg\nbpIkvzOzp5Mk2UCYvv75XC5nSZJsBHZMkuTuXC633utrdWK5bSQ8+n1CkiSP5nK5R5IkeRXhGnF1\nkiTbEB44eIAwGPZ2f+S7vBLX2G3N7PdJkrw6l8utSZJkEvBRQiNocS6XeyGXy3mdrZOPwXEAxL+8\nsrNe7kSYYApgT+A1wMuA78SeCPyvterYcDTk1cDHzOyvks4kTMr3dWA/Qu9NOjfIhtbktO0sJ9yC\n+ghwtpndKelTQHp7ZBD4tsXIyV5fqxNvhdwjaXtCj+OnCbHQvgkg6RWEWye7xR5HiE9LudJKXGPT\niQ7frhBXbjmwhz9x1hzeg+OKSpLkYOCFJEnOJTRuLjKz3+ZyOY/wW6ckSV5GKNMuwhib1Wb2+1wu\n93gul/O/fmuUy+XWxqnsT0yS5MlcLvdwkiQHAY/kcrlluVzuuVwu52NB6pTL5V5MkmQFcESSJLsl\nSfJ8Lpd7OkmSl8zs0Vwut6bVeWxnmWvs5wjX2EVmtjyXy/2lxVnrGH5fz20hDnS9jfBEykIzu6bF\nWWp78bH7NwNzCV3/t5rZra3NVWeQdBRhfMh44Foz+11rc9Q5JE0kjG/al9D7+F8tzlJH8Gvs6PAe\nHLeF2MOwDfApM7ur1fnpBLlcbkOSJAPAS8D3zGx5q/PUKXK53ANJkvwW+GUcqOmaJEmSXQi3o04z\ns0danZ9O4ddY55xzzjnnnHPOOeecc84555xzzjnnnHPOOeecc84555xzzjnnnHNuC/8/8kitBIfQ\nRPMAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f21917ff4d0>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```python\n",
      "import nilmtk.preprocessing.electricity.building as prepb\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Select data for a specific time period\n",
      "\n",
      "```python\n",
      "building = prepb.filter_datetime(building, '7-13-2013', '8-4-2013')\n",
      "```"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Resampling"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "building = prepb.downsample(building, rule='1T')\n",
      "electric = building.utility.electric\n",
      "bstats.plot_missing_samples_using_rectangles(electric)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Ry7VUn4aDanW6iOXaDo0+C6JcW6fdZdtIXaj1DKjVRbUR8EKVY2vJWkhK7ge2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\nd/LKAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f2191ee1a90>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Fill large gaps in appliance data with zeros and forward-fill small gaps"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "building = prepb.fill_appliance_gaps(building)\n",
      "\n",
      "# Now plot missing samples again:\n",
      "electric = building.utility.electric\n",
      "bstats.plot_missing_samples_using_rectangles(electric)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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UKRPr5jL9ruewfRNwMo0vVcXgPEx1Z9qAkptiEjCrr+QmIiIintHfXVQA2L6/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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f21908491d0>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Detect gaps in mains and mask these out from appliance data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "building = prepb.drop_missing_mains(building)\n",
      "building = prepb.make_common_index(building)\n",
      "electric = building.utility.electric\n",
      "bstats.plot_missing_samples_using_rectangles(electric)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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25ybR9UnKs5YDIGk28AdaknSm/FMPS3r3JJ4bBEEQBAPHuKeoUobuPW03k1je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\nTOCTaSYqCIIgCII2rNNkm7b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JR7LMx09olVOH/H6Mslkn3Yz90W927aU4KGVjumi89pLe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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f2191a9c790>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Sum the split-phase supply in REDD"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "building.utility.electric = building.utility.electric.sum_split_supplies()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Remove appliances which contribute less than 5% energy"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Hack to trick NILMTK into thinking that REDD mains measures\n",
      "# active power not apparent. This will be handled much better\n",
      "# in NILMTK v0.2\n",
      "mains = building.utility.electric.mains[(1,1)]\n",
      "mains.rename(columns={Measurement('power','apparent'): \n",
      "                      Measurement('power','active')}, \n",
      "             inplace=True)\n",
      "\n",
      "building = prepb.filter_contribution_less_than_x(building, x=5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Common Measurement:  Measurement(physical_quantity='power', type='active')\n"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "electric = building.utility.electric\n",
      "bstats.plot_missing_samples_using_rectangles(electric)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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IuB6YAJwO/Eeq6yagJyJOScdfkMp0Qgm4t0N1mZmZFdqwJjgR8TDwoKSN6uw+CNhY0g6S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\nmW3wF/QcW6Phe00RAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f2194c37410>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Disaggregation"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Split a building into training and test sets"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```python\n",
      "from nilmtk.cross_validation import train_test_split\n",
      "```"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "train, test = train_test_split(building, train_size = 0.5)\n",
      "# train and test are now both Building objects"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Training"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```python\n",
      "from nilmtk.disaggregate.co_1d import CO_1d\n",
      "```"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "DISAGG_FEATURE = Measurement('power', 'active')\n",
      "\n",
      "disaggregator = CO_1d()\n",
      "disaggregator.train(train, disagg_features=[DISAGG_FEATURE])\n",
      "# Below is the learnt model"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "{ApplianceName(name='dishwasher', instance=1): [0, 589],\n",
        " ApplianceName(name='fridge', instance=1): [0, 185],\n",
        " ApplianceName(name='kitchen outlets', instance=1): [0, 21],\n",
        " ApplianceName(name='kitchen outlets', instance=2): [0, 28],\n",
        " ApplianceName(name='lighting', instance=1): [0, 86],\n",
        " ApplianceName(name='microwave', instance=1): [0, 947],\n",
        " ApplianceName(name='washer dryer', instance=1): [0, 1957]}"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Export appliance models to JSON"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "disaggregator.export_model('model.json')\n",
      "!cat model.json"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{\"fridge_1\": [0, 185], \"washer dryer_1\": [0, 1957], \"kitchen outlets_2\": [0, 28], \"kitchen outlets_1\": [0, 21], \"dishwasher_1\": [0, 589], \"microwave_1\": [0, 947], \"lighting_1\": [0, 86]}"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Import appliance models from JSON"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "disaggregator.import_model('model.json')\n",
      "!rm model.json\n",
      "disaggregator.model"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "{ApplianceName(name='dishwasher', instance=1): [0, 589],\n",
        " ApplianceName(name='fridge', instance=1): [0, 185],\n",
        " ApplianceName(name='kitchen outlets', instance=1): [0, 21],\n",
        " ApplianceName(name='kitchen outlets', instance=2): [0, 28],\n",
        " ApplianceName(name='lighting', instance=1): [0, 86],\n",
        " ApplianceName(name='microwave', instance=1): [0, 947],\n",
        " ApplianceName(name='washer dryer', instance=1): [0, 1957]}"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Disaggregation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "disaggregator.disaggregate(test)\n",
      "predicted_power = disaggregator.predictions"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "128\n",
        "{ApplianceName(name='fridge', instance=1): [0, 185], ApplianceName(name='washer dryer', instance=1): [0, 1957], ApplianceName(name='kitchen outlets', instance=2): [0, 28], ApplianceName(name='kitchen outlets', instance=1): [0, 21], ApplianceName(name='lighting', instance=1): [0, 86], ApplianceName(name='dishwasher', instance=1): [0, 589], ApplianceName(name='microwave', instance=1): [0, 947]}\n"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "predicted_power.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>(dishwasher, 1)</th>\n",
        "      <th>(fridge, 1)</th>\n",
        "      <th>(kitchen outlets, 1)</th>\n",
        "      <th>(kitchen outlets, 2)</th>\n",
        "      <th>(lighting, 1)</th>\n",
        "      <th>(microwave, 1)</th>\n",
        "      <th>(washer dryer, 1)</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2011-04-27 19:10:00-04:00</th>\n",
        "      <td> 0</td>\n",
        "      <td> 185</td>\n",
        "      <td> 21</td>\n",
        "      <td> 28</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-04-27 19:11:00-04:00</th>\n",
        "      <td> 0</td>\n",
        "      <td> 185</td>\n",
        "      <td> 21</td>\n",
        "      <td> 28</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-04-27 19:12:00-04:00</th>\n",
        "      <td> 0</td>\n",
        "      <td> 185</td>\n",
        "      <td> 21</td>\n",
        "      <td> 28</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-04-27 19:13:00-04:00</th>\n",
        "      <td> 0</td>\n",
        "      <td> 185</td>\n",
        "      <td> 21</td>\n",
        "      <td> 28</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-04-27 19:14:00-04:00</th>\n",
        "      <td> 0</td>\n",
        "      <td> 185</td>\n",
        "      <td> 21</td>\n",
        "      <td> 28</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 7 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "                           (dishwasher, 1)  (fridge, 1)  (kitchen outlets, 1)  \\\n",
        "2011-04-27 19:10:00-04:00                0          185                    21   \n",
        "2011-04-27 19:11:00-04:00                0          185                    21   \n",
        "2011-04-27 19:12:00-04:00                0          185                    21   \n",
        "2011-04-27 19:13:00-04:00                0          185                    21   \n",
        "2011-04-27 19:14:00-04:00                0          185                    21   \n",
        "\n",
        "                           (kitchen outlets, 2)  (lighting, 1)  \\\n",
        "2011-04-27 19:10:00-04:00                    28              0   \n",
        "2011-04-27 19:11:00-04:00                    28              0   \n",
        "2011-04-27 19:12:00-04:00                    28              0   \n",
        "2011-04-27 19:13:00-04:00                    28              0   \n",
        "2011-04-27 19:14:00-04:00                    28              0   \n",
        "\n",
        "                           (microwave, 1)  (washer dryer, 1)  \n",
        "2011-04-27 19:10:00-04:00               0                  0  \n",
        "2011-04-27 19:11:00-04:00               0                  0  \n",
        "2011-04-27 19:12:00-04:00               0                  0  \n",
        "2011-04-27 19:13:00-04:00               0                  0  \n",
        "2011-04-27 19:14:00-04:00               0                  0  \n",
        "\n",
        "[5 rows x 7 columns]"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = test.utility.electric.mains[1,1][\"2011-04-27 20:00\":\"2011-04-27 23:00:00\"].plot()\n",
      "ax.set_ylim([150,450])\n",
      "title('aggregate power demand')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "<matplotlib.text.Text at 0x7f2191e39850>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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79zBjxgxKSspob2/D5/Ph8XjYu3cvFRUzAVi48DR+/ev7SU1No7x8BlOnTudXv/ofbr/9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\nMzPrPP8DJJrnxAWs0gwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f2191d2cf50>"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = predicted_power[\"2011-04-27 20:00\":\"2011-04-27 23:00:00\"].plot()\n",
      "ax.set_ylim([0,200])\n",
      "title('predicted appliance power demand')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "<matplotlib.text.Text at 0x7f219110e390>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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QZGa2kL6KAemn2In3virzlXPzt7OAkHKuUTx9hmEta+FtQ25OL47q359D+mbVWzvivZ/i\nBemn2LmFK6pdJwF1giA4Gk/k3Haqm8NuLZMMdYITEHEXBMHRWGIdLtj+aJZ7xCuJ4hESGRF3QRAc\njceovM2Fu+WjZ6sLWO4SpCskMiLugiA4mtCUs4FX/tBAusBy8cYLDkLEXRAExxOp25VWeah1bo+5\nRxmjF4REQ8RdEAQXEJ5b1poGF7k6PAO9eOWFREbEXRAEF2DY/waC5aIod0D/zRq2EYQEQcRdEATH\nE+lqjxotb4T/FW0XEhkRd0EQXEMwWj4ioC5sG4msExyAiLsgCI4nUrCjT3OLGHNv2CYJQoMi4i4I\ngmsIToWLFi1vBKLlq64ShERDxF0QBMcTtNyNgFXuryLeQdteAuoEByDiLgiCCzBC/o0eUFe5pUyF\nExIfEXdBEFyHGTnPHSqnwkkSG8EBiLgLguACIgLqovrkxVQXnIOIuyAIjseI+Ou3C8eEy3l4yVdx\nywuJjIi7IAjOx3a1B1zuwalwRuX0t0pvvATUCYmPiLsgCI7HiAyoqyn9rCSxERyAiLsgCK4jakBd\nAJnnLjgAEXdBEBxP0HIPpp+tPkOdIRnqBAcg4i4IgguILBwTsNyjZKgTt7zgAETcBUFwDVUC6qJv\nFcM2ghDfiLgLguACIiz3aPPcI0u+CkICI+IuCILjiZznHj2gTqrCCc5BxF0QBBdghP2JmlveHnP3\nEIy6E4SERcRdEATXUDnLrXblFm0XEhkRd0EQXEB4hrpAtLwZnP4W6rqXQXch8RFxFwTB8UTKdbRI\neDOo7gG3vNjuQuIi4i4IgvOJmALnp2pAXaCMTCDdvEi7kMiIuAuC4ALCc8tHncMeSGITfBBojHYJ\nQsMg4i4IgnswLIu9Mlo+UB0udBsZcxcSHxF3QRAcT2S++Jqi5T0SUCc4ABF3QRBch7+mJDaBP+KX\nFxIYEXdBENyDPa5uYmKaRtR1kqFOcAIi7oIguIDwaPmqVrlBsOSrIRnqhMRHxF0QBMdTOYXdngoX\nzS0fzFArY+5C4iPiLgiCCwi42s2wv9E3Fbe8kPiIuAuC4BoCNnmVqXAhK4N2uwTUCQmMiLsgCC7A\nzilvZ6arMuZuQHDMXQLqBAcg4i4IgmsIWu5R0s8SUkQGxHAXEhsRd0EQHI8ROeZeg3IbclsUHIBc\nxYIguIiAuEeLlo+SilYQEpSkxjiJUqoDcC8wUGs9yF52JzAiZLP7tNaf2OtuBFoAbYD5WuuZjdFO\nQRCcSrhi+2sYUY+08gUhEWkUcQeGAu8DB4QsM7XWR0VuqJQaDByptT5JKeUFfldKLdBa5zdSWwVB\ncCpGDW55I2KZaLuQwDSKW15r/S5QGLlcKXWLUup6pdS/lFLN7MUnA4vs/XzA74Rb+IIgCHUi0hqP\nHlBnbytV4QQH0JRj7u8AT2itHwMKgKft5ZmEPwjkA1mN3DZBEBxItelnDSrructUOMEBNJZbvgpa\n62Uhbz8HbrRfb8Eabw/QEviztuNlZraobRPBRvoqNqSfYife+8rjseyY9ObJZGa2YJ/1SYAZNhQf\nsHT2SU0GIC0tpd4/V7z3U7wg/bT3NJm4K6Ue1lr/y37bG8ixX88Gbre3SQb6AV/Wdry8vIKGaKbj\nyMxsIX0VA9JPsZMIfeX3m+CFoqJS8vIKKNpdCqQE1xtUBtmVlvmAym3ri0Top3hA+ql+aKxo+eHA\nOKCDUuoW4HGgQik1EctSHwD8A0Br/Z1S6nOl1H1Y0fLXSTCdIAh7RzA1jfVv1HnuksRGcA6NIu5a\n6y+pan3fUsP2jzZsiwRBcCMBUY9aFc5GqsIJTkCS2AiC4HiCch2YCodJaA338I2wtxGExEXEXRAE\nx2MGIuCri5a3t4IQy1388kICI+IuCILjibTc/SHCbUaUfzWknrvgAETcBUFwDZUyHjnPvdJFL2Pu\nghMQcRcEwQWEu9r9pr+qaR6p6WK6CwmMiLsgCK7BDAuoi45kqBOcgIi7IAguIKIqXNSpcBFj7hJQ\nJyQwIu6CILgG0y4YU1NVOBlxF5yAiLsgCC4iUBXOjFwUdNVLVTjBCYi4C4LgeCLnrtfscjdCNxWE\nhETEXRAE92CERMsHsVXcCPyRgDoh8RFxFwTBNQTc8VXnuVtLIdQtL/IuJC4i7oIguIAo89yrIGIu\nOAcRd0EQXENwnrtpF44xQgvHSMlXwTmIuAuC4B7MKNHyguBARNwFQXABdpBc0HKvvp67RMsLTkDE\nXRAE9xAcc6++5GtweF6seyGBEXEXBMEFhEfARxduEXPBOYi4C4LgGoJT4cIs92oEX7ReSGBE3AVB\ncBGBgLrwMfdoKWdF2yvx+2uKUXA38do3Iu6CILiACLd8TWPu1bx1IyUlJTz11GPk5+eHLV+06CvO\nOGM0ubm5ANx//12sXKmjHqO4uJgHHrib+++/q0Hb2tDnKSoq5Nlnn2L06OPDlr/33nQWL/6xQc65\nNyQ1dQMEQRAamsq56yHR8gHxNsM3iseyMdM+y+GH5Vvq9ZiH9s3izJG9atxm4sRHOOGEUbRu3Tps\n+eGHD+PNN98Ivr/lljuqPUZaWhp//etJzJ07a+8aXAsNfZ5ff/2Zo446mjlzPgxbfvrpZ3LVVRPo\n0aNXlX5qSkTcBUFwDX6j+nnukWPubo+WLy4u5ptvvuamm24DoKysjAcfvIe2bdvRrl0GRUWFAOTk\nrGTixEc46aRTOOGEk3n55efxer0AlJeXc8kll2OaJlu35vHUU4+Rk7OSY4/9K6NGncrLLz/PvHmz\nufnm2+nXb3/OOec0vvjic3777ReeeWYi11xzA8uX/87atatp27YdubmbueGGm/F6vXU6D8BLLz2H\nz+fD6/WSlpbG2LHjmT37Q154YRKjR5/Oxo0bWLfuD1588bWo/TFkyDA2b94Udd2AAQfwySfzGDPm\n7Hr9DvYGEXdBEByPadvjRo1V4SLEPY60/cyRvWq1suubjRs30LJly+D7mTPfJz29OVdeeQ2maTJ9\n+lsA9OrVm7/85ZDgdh9++B5PP/0cXbp0Y8mSX4PLd+/ezT//eT27du3kn/+8jFGjTuWiiy5l/vy5\n7LdffxYu/II2bdqwcOFCunbtw5AhQ+nXb3+2b9/OqaeejmEYTJz4KN9//w1Dhgyr03m+++4bli1b\nwuOPPwPAVVdNYNCgIZx00inMmzeb/fbrz0UXXcqKFcv3qK8yM7PIyYk+LNFUiLgLguB8Imq215ih\nLh798k1AeXkZXm+lRKxdu4bOnTsDVgBix46dou5355338dxzk9ixYxtjxpxN//4DMQyDzp33BaBV\nq9YUFxcHjzNkyDC++moBK1eu4JJL/sGcOXPYb7+NjBgxEoDU1FSeffYpWrVqxdq1q+nTp2+dz7Nq\n1UpKSkqYMmUyAFlZ7dm5c0ewzd26dQcIHruueL1eSktL92jfhkLEXRAEF1E1Q11Q5o34tdybgqys\nDuTn7wq+79atO2vWrAKsCHHLRV3VE1JUVMT99z/Cjh3bueCCsRx99HGYphl1RgLA0UcfywsvPMug\nQYcxaNBhTJz4MF5vStCdftttN/Haa2+SldWeoqKiPTpP796KpUt/Y9y4CwBYvPjH4ENAfZCfv4sO\nHTrW2/HqAxF3QRAcT6BGe2AKXLR57hIeH05GRgaZmVls3ZpHRkYmo0adygMP3M3EiY/SsmVLmjVL\n4733pnP88Sfy668/s2bNag49dDDz5s1i9eocSktLGTPmbMrLy5k/fy6rVuWwfPnvrF6dQ1FREQsW\nfM6IEUfRv/9ANm3ayBFHHElSUhJDhw6lTZvMYDtOPfV0HnvsQQYOPJClS39j/fp1DBkyrM7nWbZs\nKc8/P4m0tDQKCvK57LKr+OGHb8nNzWXGjGmMHXs+Ho/B2LGnM2vWJ1X6Y+VKzfz5cyktLeP111/h\npJNOoV27DACWLVvKhRde0mjfTSw4wgFlmqaZl1fQ1M1ICDIzWyB9VTvST7GTCH11zze/sTtpH/ol\n53DegSdwx6IHWf/1gRgppWQOVviK/BTt+ISSnAEcc0hnPvlxA8cc0pmxx6h6a0Mi9FMkOTkreffd\nadx44814PI0zc7op+2nqVGsGwNix58W8zw8/fMtPPy3m0kv/0VDNqpasrJbVarhY7oIguAYzYLlH\nWumGtTT4svKtq+nVqzd///sE8vPz42qaV0Nx5JEj6dQpu077tG2b0STCXhsi7oIguIZgQF0NA+ri\npA8n4Hp2A3UVdoCePRt3FkOsSIY6QRBcQKCMa2geeYNQCQ+UgzWcMVopuBwRd0EQXETAco+WD1zS\nzwrOQcRdEATXEL0qXADJUFcd8VocpSaif8fuQcRdEAQXYGeoi1oVLlIEzKhL3Uhk4ZipU9/g+ecn\ncdddt7Jhw/oq28+e/SHvvvt2leXz58/lhBNGNkgbN2xYz803X8+tt/47uMw0TZ555gny8uo3H38i\nIeIuCIJrqGK5hw2vi1s+kokTH2HEiKODkfIffvgeEyZcwW233U12ducq25900imcfvpZVZYfd9wJ\nNG/evEHauGzZUoYMGRa2zDAMzjlnPPfee2eDnDMRkGh5QRDcg1nNVLjQZdVkUnMbkYVj5s6dxc6d\nO3j11Rc59NDBTJv2Jps3b+TAAw9myZJfGDz4cDZt2ghYVeJ27drJQw/dR9eu3Wjbth0VFRXBY7/x\nxmTWrl1N167d+PXXn0lOTuGaa26gqKiIRx99k06durJu3VrGj7+o1gj24477K3PmzKyyPCMjg7Ky\nUlatyonbiPaGRMRdEATXYFYXUBdFz+PJcJ+RM4uftvxWr8c8KGsAp/U6udr1kYVjTjjhZF555YVg\nJraMjEwuv/xiLrv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WM2jQYcyfP5dVq3JYsWI5ffr0Dbb55JNH88wzT/DKKy9QUFDAmWeO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\ns8GEAR+lAQ07ZOURftOfvRXr9iNstfwyss3NwihOhOW7szP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DZ40x788++3ngvDHmXaXz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\nKIqiKIqiKIqiKIqiKIqiKPXj/wOtUK9wHtDxDAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f21943d3cd0>"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "app_ground = test.utility.electric.appliances\n",
      "ground_truth_power = pd.DataFrame(\n",
      "    {appliance: app_ground[appliance][DISAGG_FEATURE] \n",
      "     for appliance in app_ground})\n",
      "\n",
      "ax = ground_truth_power[\"2011-04-27 20:00\":\"2011-04-27 23:00:00\"].plot()\n",
      "ax.set_ylim([0,250])\n",
      "title('ground truth appliance power demand')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "<matplotlib.text.Text at 0x7f2191cd37d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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EWG04cGB31H0LW6F+EXH3kYLiQkKtIQHdM1nC8kKg4hTr8sVnPYXlA2jM3Wq0\nD5HQfPARuEpVw+QX5wd0SB6kSp0QfHhMqKsnW2qDEIsNQJLqghARdx/JLy4I6GQ6kDF3IYDx4rp7\n7Apn8b+ucN6wWZ3iLp57sCHi7iMFxQUB77lHypi7EGR46+cOBEQSmjMsXyyd4YIOEXcfybcXBHQy\nHbiXoJWwvBBYOLPfy3rpnjx3l7jXhWG1jITlgxcRdx8othdTZC+ikTWwxd1ZglbC8kKw4MBePqHO\nXBAAjntJQp2E5YMOEXcfKLCbBWxCAlvcAVPcswIiJCkIlVFhWL7Oral5XGPuEpYPOkTcfcBVwKaa\nnrvd7j9zTKPMErT5dVSC1p/ujRB4eJznbsp7IBSyCbGYY+7iuQcdIu4+UN3Ss3l5ebz22ktkZmaW\nWr5mzWouu2wMaWlpADz77BPs2KE9HiM3N5fnnnuSZ599ohqW+47zPL9//BNQ8xnzOTnZzJjxGmPG\nXFBq+VdfzWX9+t9r9FyC4A1Pteg8JMsHDCFWGXMPVkLq24C6YM4Pify27WC19y9yFJGXP4Q1mxqx\nYcUaAM7sEc/lw7tWuN/06S8wcuRooqOjSy0fOHAQn376P9f7hx56zOsxwsPDufDCUSxZsrDa9vuC\n8zy7Pn8LMDLm48Nja+z4GzduYNiwESxePL/U8nHjLmfy5Nvo3LlrufskCDWF18YxDgdWLzPbA2Fk\nymaRIjbBSlCI+wlj/pFXpaFEbm4ua9f+zAMPPAJAQUEB06Y9RYsWMcTExJKTY3jGiYk7mD79BUaN\nuoSRIy9m5sy3sdmMp+3CwkJuueV2HA4Hhw6l89prL5GYuIPzzruQ0aPHMnPm2yxduogHH3yUnj1P\n5qqrLmXu3AVs3bqZN96YzpQp97Nt21Z27kymRYsY0tL28/zzzwJ4Pc/xzFyOLTnEM3Me5h+jxjN6\n9FgA3nvvLYqLi7HZbISHhzNhwkQWLZrPO++8yZgx40hN3cvu3bt4990PPd6PAQMGsX//Po/revc+\nheXLlzJ+/JU+319BqAnsFRSxCQBtd3nuEpYPPoJC3C8f3rVSL7siNmdsZ8ZfCxnd+UIuSBjo0z6p\nqXuJiopyvV+w4GsiIppy551TcDgczJ37GQBdu3bjtNPOcG03f/5XvP76W3TokMCmTRtdy48fP85d\nd93HsWNHueuu/2P06LHceOOtLFu2hJNO6sWqVT/SvHlzfvllDX36nMqAAWfTs+fJHD58mLFjx2Gx\nWJg+/UW96YNfAAAgAElEQVRWr17NSSed5vU89oJi2o7vxujW5/HptJmMHj2WX39dy5Ytm3j55TcA\nmDz5Nvr1G8CoUZewdOkiTjqpFzfeeCvbt2+r1v2Ni4snMdHzsIQg1D5lxT2Axtxd2fISlg82gkLc\nT5SSpjG+j7kXFhZgs5Xc3p07U2jXrh1gZOe2bt3G436PP/4Mb731JkeOZDB+/JX06tUHi8VCu3bt\nAWjWLJrc3FzXcQYMGMTq1SvZsWM7t9wyieXLv+XIkcMMGTLcsLlRI2bMeI1mzZqxc2cyR470rfA8\nbdq0IRsoauRwnScpaQd5eXnMnj0LgPj4lhw9esRlc0JCJwC6d+/h8/1xx2azkZ8vPeSF2qfseLrH\nee4B5LrbnPPcJSwfdIi4+0BBNdq9xse3IjPzmOt9QkInUlKSACND3AhRG78e7tPOcnJyePbZFzhy\n5DDXXz+BESPON36AvGT5jBhxHu+8M4N+/c6iX7+zeOmlaYSHh7vC6Y888gAffvgp8fEtycnJcZ3L\n23lCbWGAvVQhm27dFJs3/80111wPwPr1v7seNmqCzMxjtGrVusaOJwhl8TZ+7sCB1cvfVgBou3ju\nQYyIuw+UZMv7Lu6xsbHExcVz6FA6sbFxjB49lueee5Lp018kKiqKJk3C+eqruVxwwUVs3LiBlJRk\nzjyzP0uXLiQ5OZH8/HzGj7+SwsJCli1bQlJSItu2bSU5OZGcnBxWrlzBkCHD6NWrD/v2pXLOOUMJ\nCQmhX7+ziI9v6bJj7NhxvPTSNPr0OZXNm//m4MF99Op1htfz7N21G0uXKDYk/l7qPFu2bObtt98k\nPDycrKxM/u//JvPbb7+QlpbGl1/OYcKE67BaLUyYMI6FC5eXux87dmiWLVtCfn4BH330PqNGXUJM\njJGwt2XLZm644ZYT/JQEoWp4q+UQQI67jLkHMQEx6cPhcDjS02uvZOp3u37k66TF3N7nBnrF9vR5\nv8TEHcybN4epUx/Eam0Ysw7j4iKp6F7lFObyz1WPc2pcL27pPbFKx/7kE2MGwIQJ1/q8z2+//cKf\nf67n1lsnVelctU1l90kowR/u1QfbU9mRmcsTp3ch1PxbtDvsTF7xAKp5V+7ue6tr248T97H5SA7/\nPrUzEaG2GrOhPu7T+oMbmblpNpersQxp51u+UH3jD9+nhkJ8fJRXDRfP3QdKxtyrVsSma9du3Hzz\nbWRmZvrNNC9niLLYUfXiMkOHDqdNm7ZV2qdFi9gGJ+xC4OL+S+j03Mv3cw+chDqb1JYPWkTcfSC/\nGmPuTpyhZ3/Bav4Y2Ksh7lUVdoAuXao/i0EQfMWTTHtrJlPRPv6GhOWDl4YRK27gVGfM3V+xWkpC\nloIQeJQIuctzLzvPPSAGKw2c5WcLpbZ80CHi7gPOp94Qa2g9W1L72ETchYCkvB/uXOK15WsAuO42\n8dyDFhF3H3COP3ubMlMZ/tQcxflDJz8GQiBSaswdL567a8zd/wl1ToUTzz3oEHH3AbtZAMIZsvaV\nso1jPvnkf7z99ps88cTD7N27p9z2ixbNZ968z8stX7ZsCSNHDq+G5ZWzd+8eHnzwPh5++F+A8UNn\nwcKGr34hPb369fgFoSHhcczdfGj37rn7v7yXJNTJw3qwUScJdUqpVsDTQB+tdT9z2ePAELfNntFa\nLzfXTQUigebAMq31grqw0xt286fB+YfiK2Ubx8yf/xWfffYldrvdY1GaUaMu8Xic888fybvv/reK\nVvvGli2bGTBgEOvW/eJaZrPa6DbsZJ5++nFefXVGrZxXEOoFtz87V1g+CLrCFUu2fNBRV9nyZwNf\nA6e4LXNorYeV3VAp1R8YqrUepZSyAVuVUiu11pllt60r7K6wvO+ee9nGMUuWLOTo0SN88MG7nHlm\nf+bM+ZT9+1M59dTT2bTpL/r3H8i+famA0SXu2LGjPP/8M3TsmECLFjEUFZX8cf7vf7PYuTOZjh0T\n2LhxA6GhYUyZcj85OTl88slHdO7cld27dzJx4o2VZrCff/6FLF5c+tnJioXQqMYUFOSTlJQoGe2C\n3+PJCS+ZCuf579r//Xb3CnXiuQcbdSLuWut5SqmhZZcrpR4C8gEb8LrW+jhwMbDG3K9YKbUVw8Ov\ntvf+ZeJC/jz4d3V3J8vsbf7cuukuj7tvfG8u7Xqx133KNo4ZOfJi3n//HVclttjYOG6//Sb+7//u\nJD8/n4yMQxw6lO5q7Tp79of06tWbCRMmkpuby1tvvQ5AcnIi3367mNmz5wDw1FOPcMYZ/WnZshW3\n3XYDkyffS69evVm//nfeeGM6zz77QpWv12qxYXfYiYuLJyUlScRdCBh8GnO3ONf7PyW15cVzDzbq\nc577F0CK1vq4Uup24HXgZiAO2Oq2XSYQXw/2uVH1P/OyjWPKHdHhoG3bdq4WquHhHTh0KN21fufO\nZHr3/gdg9FqPjm4OQEpKSQMagDZtSl4nJyfy22+/sGHDH+Tn5xMeHu6TrWV/3GwWK3aHnZCQEAoK\nCnw6hiD4GyXz3EvjSqgLgDF3p+cuCbLBR72Ju9Z6i9vbFcBU8/VBjPF2J1HAgcqOFxcX6XXdbXFX\nAVdV3UiTp398jY0HtvLG6CcJtfk2Ha5nzy7k5GSVsstqtbje5+dHEBYWUmp9s2ZNaNw4lLi4SHr2\n7E5Gxn7i4iLJycnh2LGjxMVF0rfvyXz00Xuu/TIyDtCjR1fi4iLp0aMHY8aMonv37hQUFLB8+XKP\n96XssqZNG9GoUYktNpsViw3y8nJQqlOF9zaQCdbrrg4N/V6FJhsebFxcpGvWS6M8Y13jRmGl7G+y\n/zAAzVtEEBfRuEbtqOv7FFFg/MRbQxv+Z+SOP9naUKk3cVdK/Udr/U/zbTcg0Xy9CHjU3CYU6An8\nVNnxarMWcV6+4b0ezsj1edzdYmlCixaxbN2aTGxsHMuWLSEzM4uXXnqVsWPH8dlnH7N3byoffvgJ\nF100mtzcXObMmceOHZoVK9YwbtwEpk17in37DhIREUFERATvvvsBY8eOZ8SIC7jjjrvo2rUbWVm5\nZGXlkZ6exf33/5t3332ftm3bkZGRwfDh55KensXKlT/w3Xff8vTTz5er27x69Uq+/fY79uzZzauv\nzmDChGuxOCzk5xWwe/deEhJ6BGWdZ6lv7Tv+cK8KCgzPNT09yyXuzuG2/IKiUvbn5RUCcPhwDtbc\nwhqzoT7uU0GxYX9uXn6D/4yc+MP3yR+oq2z5wcA1QCtznP1loEgpNR3DU+8NTALQWv+qlFqhlHoG\nI1v+3vpMpgOw43nKTGVMnfoQM2e+w9SpD3L++SM5//yRrnW33jqpVE318PBwV/Kdk+eee8n1+vrr\nb3a97tfvLCZOvNHc5knatjVC8x07JvCvfz1c2na7ne+//47LL/ccuRg0aAiDBg0ptcxqsZH8/RZu\nv30yISFSoVgIHNz/gu1m2N3qpZ+73f+j8q5seaktH3zUVULdT5T3vh+qYPsXa9eiqmF32LFarF57\nqnujthrHfPHFp3TokIDdbic+viW9e5/idVur1crtt0+mdes2Ph/f4rAQ26sNZ599Tk2YKwgNEofz\nob1cEZvAwWqxYrVYKXbImHuwIW6ZDxQ77K6yrFWlNhrHPPLIU1XavirCDsaYe5P4plXaRxD8AV8e\n0AOpKxxAiMUmU+GCEKlQ5wN2hx1LNcXdH3FOhROEQMFzhTrP89xdU+ECQ9uxWUMkLB+EBI9inQD2\nE/Dc/RGbxerKMxCEQMU15u7Fmw8QbSfEYpOwfBASPIp1AjjH3IMFi8UinrsQWHh0wz3Ld1UTZxs6\nIeK5ByXBo1gnQLCJu81idXXCE4RAoaxkV1qhLkDi8jarjLkHI8GjWCeAkVBXtaYx7vhTy1fw3zH3\nQPkxFmoeT98Mr1PhKtjHHwmxhkhYPggRcfcBu8NerVCde8vXL7/8gssuK9/1LScnmzvvvNX1fsmS\nhWRnZ1d4XG/HqimsZvlZX/jzzz/YsUMDRvnbu+++3VUf3xs7dmj+/POPattXtk0tGML+xhuvSJta\noQq4+sKVWhpw4m6xSVg+CBFx94HqJtRNn/4CQ4aMIDo6mksvvczjNhERTXnjjXdc7xcvXkB2dsXV\nmbwdq6awmmPuvnjC69f/TmKiIe6dO3fllFP6VrrPjh3bT0jcnW1q3bFYLFx11USefvrxah9XCC5c\n2fJewvKBou4h1hCKxHMPOoJinnv6F5+R9ftv1d7/0rxjWCwWkr+5z7Us8owzibvsSq/7lG356s4n\nn/yPX375mbFjx5Ofn8err77E0qUrWLfuF9LS9jNnzid06JDA2LHj+OabL9mzZzfNmkWzZcvfPPro\n0zRp0oTi4mJmznyblJRkIiIiePDBRwFYuXIF69atpU2btqSlpTF58j1ovY0XX5xG9+49iIwMZ9Om\nLUyZMpUePXqWsqu4uJg335zO9twdHDh8gPlZXzFmzKV8/fVcPv74I774Yj7r1v3CCy88y+uvv0Nh\nYQEbNqwnMXEHaWn7ufrq60odLzk5qVwL2iZNwlm1aiXZ2Vl88MG7jBlzKV99NRebzRj2KCws5JZb\nbq/w8/DUphYgNjZW2tQKPuPZby9ZEiDajs303B0OR5ULcQn+S1CIe01Q1T+Jsi1fnaSkJHPs2FFe\neeVNl6C9/77huffrdxatWrXm8suvplWrVuzcmcK8eZ/z0UefA7By5Q/YzcSYY8eOMmHCRJo0acLE\niVeQmWlU6H355ef54ov5hIWF8f777/DNN19y2WVXMnjwUAoLC3n44QeYM+crli5dWE7cFyz4muLi\nYnpdeAa2w5q5H3xO376nMXbseD7++KNSNgK0b9+Bvn1Pp3XrNowcWb797fPPP+2xBe3gwUPZv3+f\nq/3t/Plf8frrb9GhQwKbNm2s4p0ujbSpFTzhoKKEujLz3Mus93ecJWjtJ5g7JPgXQSHucZddWaGX\nXRn3//QYLRpH81C/e3zex1PL12PHjjFr1nsUFOS7hL0ikpOTaNOmrev9kCHDXa+bN29BkyZNAGjW\nLJrc3FyOHMnAYrEwZ84nAGRmZpZq+9qhQ0cAoqObk5ub6+F8ibRt245DFqNdVqvWrUlOTqJDhwQf\nr7r88Ty1oC0b7n/88Wd46603OXIkg/Hjr6RXrz6VHtubByJtagVfKSli42XMPTC03dX2tchRjA0R\n92AhKMT9RLE7iqs8FS4+vhWZmcdKLYuKiuKJJ57lmWceZ86cT7j88gnl9rNabTgcdpKTk+jUqTP7\n9+9zrfvxx+859dTTiY6O9iBuDtq2bU9YWBgTJkzEarWSmrqXQ4cOGWvdfqm8jaV37apITNSE94wH\nYP++VDp3Lu8BHzx4AGfQ0mq14nA4OHToECEhIaWO3bVrNwYPHkaXLl0pLCzkp59WAGCz2XA4HGRl\nZZGbm0Nubi7PPvsCR44c5vrrJzBixPle7qrb1Xq5hszMY7Rs2arS/YUgw4PrXuK5l14eaJHrEEtJ\n85hGtrB6tkaoKyShzgfsDjvWKt6q2NhY4uLiOXQoHTBC3rm5uaxYsZwBAwbxzjsz+PDDmSxbtpTs\n7Gy++eZLAPr3H8Ds2bOYM+cTOnXqzLhxV/Daay/x4Ycz2bkzhejoaBYs+JqcnBxWrlzB+vW/k5aW\nxqJF84mKiuKuu+5l+vQXmD17Fp9+OpsOHTqwZ89uNm7cwM8/ryI9PZ1ly5aQlJTI9u3bStl88cVj\nCAkJ4e9Fv5O6WPOP8Ze5vP0LLriI6dNf4Ouv5xEeHu6yt2/f01m1aiVvvfU6O3cmu85z6FA6Dzzw\nKHPnfsbs2bOYMeM14uNbAtCz58ls2bKZGTNeIzPzGEuWLOCjj95n7tzPGT/eiLCsXPlDqWx4d1av\nXsmaNavYs2cXn3zyP9fyoqIi0tLSfErqEwRvnrsT/5sM6hmX5y5z3YOKgHhGdTgcjtrs/3vXigfp\nENmO+8+4o0r7JSbuYN68OUyd+iBWa8N4jvKlV/J7f/+PP9P/ZtqgR4kMq/sGMna7nccf/zfjx19B\nnz6n+rzfrFnv0a1b9xrpZic9pX3HH+7Vf7fsYV9uHk+d0c21bE9WKtN+e5Vh7QcxvlvJ1NLvUjNY\nse8wt/RoR6fIJjVmQ33dpw+3fMa6tPU8OeBBYpo0r/PzVxV/+D41FOLjo7xquITlfaC6Fepqq+Vr\nbeO81voqZFOdNrV2u50hQ4bTqVPnWrRMCCRcYXmvY+6BMejuDMsXO2SuezAh4l4JdocdB44G1fK1\ntrFaSrJr64uqtqm1Wq0i7EIllBbxShPq6sKkOkDC8sFJw4gVN2CcAhdsteWhfsVdEGobb7XlS9YH\nBjZzKlyReO5BRfAoVjUJRnF3tsCU5jFCoODAUX6eu6ne5Tx3V+eY2rerLgixGJ57sXjuQUXwKFY1\nCU5xF89dCHy8doUrs97fcRaxkfrywUXwKFY1cQpcdcfcwT+7woH/iXugJEAJdYW3fu4VrfU/bJaS\nIjZC8CDiXgnFJ+C5+29XOOPnzRdxr+uucFu3bubJJx/h009n8/zzz7BgwdeAdIUTqo7Xlq+ufu51\nbVHtIJ57cCLiXgknEpb3365wvofl67orXEZGBpdffhVXXXUN9933L1chHOkKJ1SEw1G+8pzXrnDO\n9XVgV13gzJaXMffgIiimwq35IYnkbdXz6IoddlT+UApsYcz+aa1reece8Qwc3sXrfv7cFW5f0UH2\npe1gefq33HjFTQ2qK9ygQYNLvbfZbISEGF9j6QonVA1vfeECS95dnruE5YOKoBD3mqFqxfz8uSvc\ngDFDydrlYNnMRZw7YESD7Qo3b97nTJx4I+HhEa5l0hVO8BWvCXUBFpZ3jblLWD6oCApxHzi8S4Ve\ndkUczD3EE7/8h4Gtz+TqniN83s+fu8I5kwdjW8Y12K5wy5YtJT8/n4kTbyy1XLrCCb7idczd/D9A\ntN3luUtYPrgICnE/Eao75u7PXeF69DO6wh1MO9Agu8ItWPA1x48fZ+LEG0lKSiQsLIz27TsA0hVO\n8I732Fugi7vxM18oRWyCCkmoq4QSca9aH2R/7gq3+qsfSF2sOfeSkQ2uK9yqVT/y5pvTWb16JZMn\n38aTTz5MRobxACNd4QRveBJqrwl15vvACcuL5x6MSFe4StiTtY9pv01nWLtBjFdVm37mr13hlu1a\nwTdJS5h0yo2cHNOjjiwrQbrC+Rf+cK/e2LybQ3kFPH56SSRqc8Z2Zvw1kzGdR3J+wjDX8jUHjrJw\ndzpXdWlF7xaRNWZDfd0nb9fZUPGH71NDQbrCnQB2M8PUW/3pipCucNU8v3SFE+oEV/3ZUgSEx+OG\nsyuchOWDCxH3SnAm3diqGJZ34o9d4VxhPOkKJwQQZWvIe+0KF2jZ8pJQF5Q0jFhxAyYYa8tbqlCh\nThD8AY9j7pXWlg8MQq0yFS4YCR7FqibOsHwwibu0fBUCEm8V6rwUsQmUxjHOSJwUsQkugkexqklx\nUDaOqVzc/e2ahCDHQ4zdm+duDbBB95Lys+K5BxMi7pXgFDhLDTeOufnmiZV2MZsx41UmT77N47qy\nDWbuvXcyR48erbKNnijbFe63337lhhsmMG/eHNc2Bw8eZMaM1yguFm9A8A/KV4ZwLves5oEy5l7S\nOEb+VoMJEfdKOJGWrxU1jnnvvY8qzcC/9NLLva4r22Dm5Zdfr7GMfGfFLrvDTm5uDjk52XTr1r3U\nNq1ateLkk3u7SucKQkOmwnnuAV7EpiQsL557MCHZ8pVQ3YS6ihrHrF69kldffYnXX3+HVq1asWTJ\nQn7+eRVKdScpKZHDhzO4556pNGkSzvHjx3nzzVfZtSuF7t17ctNNt5VrMNOpU2deeeUF7r77PuLi\n4nnmmceJiYklJiYGrbcxceKNDBgwiIKCAu6//37Cw6OIjIxk+fJlnHXWQO68c0op+5wPMsUOO+Hh\nEQwdOoI1a1aXu46zzhrAyy8/X2GjF0FoqARLQl2IK6FOPPdgIijE/Ujqd+Qe3VKtfSOLC/m/qHDC\nj/xCatafruXh0SfRvO15Xvfz1jgGYNCgIXz+uVH//dixo7z11uvMm7eIkJAQ3n33vyQkdKJz567s\n37+PI0cOc8cdd2O32xk/fjQ33XRbuQYzAEp1x2Kx0K5dey655B/8+uta7rnnn2zbtoUPPniXAQMG\nsWDB10RGRjJp0j04HA4WLPjaVRHOHau1dFjeG40aNcZut3P4cAYtWsRUfCMFoYFR4rmXxhJgc+Fc\nteXFcw8qJCxfKZ5/ACrDU+MYT6Sm7iU6uoWrbWmbNm1LjcU753tbrVafms04cdZadzaVAdi5M4WO\nHY1yshaLxetccvewvDuehhFCQkLIz8/32S5BqC/Kj7l79txL1gcGIRbx3IORoPDcm7c9r0IvuyJ+\nS/uTWVs+5cruF3JO27N83s9T45jyOGjbth1Hjx6moKCAsLAwUlP3uoQevP/wuDeY6dgxwTia+VDg\ncDg87peQ0ImUlBTAyHZ3b0pT6tgesuUdDke5BECHw8Hx47nExsZVcp2CUL94FOpgGXN3JdSJ5x5M\niOdeCdVNqCvbOObLL78gJyeH779fxtq1q0lLS+Obb76kWbNobrvtTh577CH+978POHLkiKsO/YIF\nX3PgQBrr1//OypUryM7OZvHiBUDpBjObN/9NYuIOli1bQlpaGmvWrOKvvzaQmrqXxYsXuI4xevRY\nsrOzefXVl/jss9k0a9bMVY1r+vQXXNnwZcV97tzPSEoy2rd+//13rmtMTNT07XsGoaGh1by7glB/\n2AmOCnVWixWrxUqxzHMPKoLCcz8RTqRC3dSpDzFz5jtMnfogl156WamM+S++GOR63blzFy66aDRg\nND9p06YdALfeOolbb53k2m7IkJKmDxMmXFvqXO+/P9v1+umn/+N6fdNNt3HTTcZ0uuPHjzNp0iSi\nooyWrkuXLiImJpajR4+SmLiDG264pdS1Oq99/Pgry43N5+fn89lnH3P33fdX6Z4IQn1Qpa5wAVbE\nBoz68uK5Bxci7pVwIuLua+OY775bys8/ryIsLIyjR48yceKN1ba3IvLz83n55Rfp0aMXGRkZXHfd\nzYSEhBAWFsqzz77oSgD0pYhNVlYmU6ZMJTKy5rpmCUJt4n3maWCPuQPYrCEy5h5kiLhXwolWqPOl\ncczkyfdW69hVJTo6mv/+97/l2imGh0eUeu8+Fc4bMs4u+BUelNrubSpcgIXlwciYl7B8cCFj7pVQ\n4rlXryucP+K8VofUlhcCCs9d4axeEuoCiRBLiITlgwwR90qw4xT3QPyT94zzWuuz5asg1CSenXDP\nSwMtWx6MjHkJywcXIu6VEIwtX30ZcxcEf6PcPHcvCXWurnABFJcPsYZI+dkgI3gUq5o4u58FY1he\nxF0IHMoLtXPMvWxY3tkVLnCk3ciWLxbPPaiok4Q6pVQr4Gmgj9a6n7msBfAckAx0Ax7SWh80100F\nIoHmwDKt9YK6sNMTNdXy1Tl33R9whuWdQxK1gb/dEyEQMeU7CIbcDM9dxD2YqKtf17OBrykdGXsW\n+E5r/by57kUApVR/YKjW+lFgCvCSUspzkfY6wG7+QVQnLO/e8tVXZs58m9Wrf6ryuWoSX7Llq4K0\njBXqmyp1hQvAbHmbOc89kIYahIqpE3HXWs8DssssvghYa75eA4wyX19svkdrXQxsBYbUgZkeOZEx\nd/eWr75y0023MWjQ4CqfqyZxjbnbT1zcpWWs0FBx9XP3WsQmcAg1O8PJUFvwUJ/z3OMB54TrTKC5\nUsoGxGEIOm7r4k/kREv2pPP34bLPFr6RW9SByIgrmZNSRIg1xbW8d4umjGzvfa532Zavn3zyER98\n8B533jmF339fR6NGjejb93TWr/+drKxMpk17mYMHD/Lqqy/QrVt3brzxVg4dSufdd/9Lx46dSE3d\nQ8+eJ5GQ0IUXX3yOHj16Ehoaxo8/fs+nn37J998vY+/ePTRt2pSjR48yefI9LF68gDfemM5DDz3G\n4MFDufzyMcyZ8zkpKfuYNu0pbr75NvLy8li1aiUdOnQkOTmJ++9/gGSdyNZX15LXI52rnx/PkiUL\nmT//Sx577Bm2b9/GunVradOmLWlpaUyefA9hYWFe74O0jBUaCl4T6rzWlg8ceXfWly+0F7leC4FN\nfYr7QYxx9UwgCjiitS5WSjmXO4kCDlR2sLg475XSmhw6hs1WvSCFxYwaG13ZSo7RpElYhefcti2V\nFi2au7a5++47+OabeVxwwXBuvvk6xo4dy1VXXc7111/NpEmTOHRoL717n8yoUSNJTU0lLi6S5557\njPPPP58LL7yQwsJClixZwrBhA9m8+QLy8vK4//770XoiBQWZzJ8/j/nz5wPwxBNPsHLlt9xwwzUs\nWTKf/v37sndvIs2aRbF06VKuvvpq+vc/k4suOo/ffvuNp556nKZNmzJr1ixWr/6eC8aOJH5QRxrl\nGNfYokUkDzzwLzp0aMntt9/IDz/8QFhYGG+88Qbff7+IiRMnVnofGzcOJTKycZl7Fgk4sFjyiY2t\nvNhPXVPR5yuUpqHfK5vNitXuKGVnxBHjoTS6WXip5QfMXJOIiEY1fl31dZ/CGzcCoHmLJjRtFFHJ\n1vVPQ/8++QP1Ke6LgIHAFxhj8gvdlj8KoJQKBXoClQ5Cl6265s7Q2GYMjW1WLSPn7pjPij2rueOk\nu2kf2dbncx44cASHw1JqG7vdQePG0aSnZ9G4cTgRETGkp2fRqFE4+/YdIj4+i6ysPHJzC0hPz2LL\nlq2MHz/BdYwBA4aRnp5FTk4+rVu3JT09i+bNW/PDD8uJi2vp2q5Fi3j+/HMjw4aNZPDg4cyZ8xX5\n+Xncd99DzJjxCjExrenRow/p6Vnk5Tl44YVXaNYsGq230alTZ44cyaV5n5Ykz/iTXbsOsGrVGu6/\n/0H++mszDge8+ebbABw6dAiHw1bhfXCSl1dIdnZ+uW2tVhv79mXgcDTy7QOpI+LiIn26LsE/7lVR\nkUHdtiAAACAASURBVB2Hw1HKzqzsPAAyM4+XWn7s2HEAj9/XE6E+75O90Pg/Lf0ozRo17NC8P3yf\n/IE6GXNXSg0GrgFaKaUeUko1Bh4CzlNK/Rv4B3A/gNb6V2CFUuoZ4FXgXq217xlpNUx1x9x9bfnq\neuXWrtX5umtXxd69ewDIz89j6dJFpbZ10rVr11LtW/fu3YNSxhj38OHn8f33yygqKqJHj57k5OSw\naNF8+vcfAMB//vM055wzhGuvvZ4zzugHGMk31lAbCad145VX/kPPnicD0LZte8LCwpgwYSLXXHM9\nl112JT179vLpfkjLWKGhUXlYPnAoafsqCazBQp147lrrnyjvfecBt3rZ/sVaN8pHiqsp7u4tX2Nj\n4/jhh+Xk5OSwZMlCWrZsRVpaGkuWLOTss89xtWtt3bota9asIisrm507U7jjjim8886b7N27l8OH\nMxg9eix79uxm48YNpKQk06lTF3r06EmHDgmMG3cFr732EhERTQkNDWPUqDEAxMXFEx4ezimn9AVg\n5MiRJCXtwmYz/thHjRrDrFnvcdppZ7B9+1aysrIZNHwoAN2H9GbFywuYOvUhAKKiorjrrnuZPv0F\n4uNbkpaWxk03GR/h9Okv0L59R8aNu7zcvXC2jM3KyiQ6ujkjRpwHSMtYoX5xeKstH5Bd4Yyf+mIp\nZBM0BMQET4fD4aitMM4n2+by8751PHrWVFqGV83DTEzcwbx5c5g69cEGM6fbl5DX8aLj3P/TY/SO\nPYn/63N9pcc8evQoDz/8T5555j80a+bbzID8/Hz+859nuOWWSbRq1cqnfeoSCQ36jj/cq5c27qTA\nbufBUzu7ln2360e+TlrM7X1uoFdsT9fypMxcZm5PZUSbFoxoG1NjNtTnffp8+1f8lLqWf/e7lzZN\nG97fmzv+8H1qKMTHR3nV8IahOA2YEyli497y1Z+oaoU6Z8tYX4UdSlrGNkRhFwIPBx6y5b147u77\nBAqusLx47kGDtHythBOtLe9Ly9eGhrMcp6/iXrZlrC/IOLtQ31Q25h5IOMPyMuYePIjnXgnSOEYQ\nAoUyLV9dS8tWqHM2jqkLm+qGENNzL5a2r0FD8ChWNaluQp0/I+IuBAPeusIFYhGbELNCndSXDx6C\nR7GqiaMGxL0myrjWJRaLBavF2qD6uUtNbOFEcODwEG4PoqlwFudUOPHcgwUR90o4kYS66jSO8YWD\nBw/w739PrbW67GvWrGbzyz+Tc7juEwH37t3Dgw/ex8MP/8u1zOFw8MYbr5CefrDO7RECF7sroa70\ncksAqrvTc5e2r8GDiHsllIy5V70ec3Uax/hCfHxLBg2qvV46AwcOolF043rx3Lds2cyAAYNKLbNY\nLFx11USefvrxOrdHCBA8pMt7S6hz3yVQCLGK5x5sSLZ8JVQ3oa5s45inn36MpKQdPPTQY0RENOWf\n/7yH2bPnsHbtambNmsnDDz/B0qWLKCoqIjQ0lIKCfCZNuhuAl19+nri4luTkZBMXF8e4cVfgcDhI\nSUlm+vQX2L59GxMn3sCAAYMoKiritddeokWLGLKzs+nWTXHBBRe5GtdMmXI3a9euw2638+yzL7js\nLSgoYNq0p2jRIoaYmFiK84pxOBxs2rSRF1+cVqpRzcCBg/jjj9945JGnaNu2HY8++gAXXHAR5557\nPq+++hLt23ckPf0AgwYNoV+/s5gx4zW+/34ZF100ms2bN5GQkMBdd93n8b6df/6FLF68oNzy2NhY\nCgrySUpKpEuXrlX6LATBMyUpde4EYhEbm0XG3IONoBD3OT8k8tu26oV0MwvaU2RvzUPbfsX9R+DM\nHvFcPty7yKSm7iUqqqQN/ZQpU7nllol069adTz75CJvNSlJSIi1btmL06LG0b9+Bnj1PcnnkDzxw\nLykpycTExPLzz6t4++0PiI2NY9Omja5jhoaGMmXKVLZt28oHH7zDgAGDWLjwG4qKirj++ptxOBxc\nc81l9O8/kAkTJvLVV3M555xzGDVqHNu2bS1l74IFXxMR0ZQ775yCw+Hg3Y/fxu6w06tXHwYPHkp+\nfj633z6Zf/xjHO3bd2TixCto164dLVrE0KVLN8aMuZS33nqD9u07cO2115Ofn8fVV1/GnDnfMGnS\nXcyd+zlXXHE14eHhJCUlVuuziIuLJyUlScRdqDIe57lXllAXONruli0v4h4sBIW41wxVm/1aWFiA\nzVZye5s2bUrHjgls2vQ3R44cYcyYcSxf/i1hYWFcdtlVABQUFDJjxmtERUWRnp7OsWNH6dSpM3ff\nfT/Tpj1Ffn4+1113k2GNxULbtu0AaNasGbm5uQAkJSWSkXGI2bNnAdC5c1cOHz7kGhro0qUL6elZ\n9OhRUpELYOfOFNq1a+c6duPmTUplyyckdHIdD+CSSy7lyy+/oFev3gwYcDYAycmJREU1c527S5eu\nZGZmEh0dTYsWLWjatCkA3bqpCu+dt6IiISEhFBQUVLivIPiKc8zdWlbczbcBpO2uMfdCKWITNASF\nuF8+vGuFXnZFvPTHDHZm7uaFYdOqtJ+nxjHDh5/P+++/w7nnns+AAWdzxx23MGDA2TRt2pSsrCye\nfvoxli1bSUhICElJiTgcDrKzs4mOjubFF18jOTmJJ554mA8//BSHw+FRBLt1U4SFhXHNNdcD8NNP\nP9KqVZtK7U1I6ERKShJgZPfnHznuEndPmeqjR4/lhhuu5vDhw/zrX/8GoEuXbsTExDB+/JUALFu2\nhGbNjG583gTbE94y4zMzj9GypVS0E2qaQCxbUxpntrx47sGDJNRVgt1hr9Y0OPfGMU4GDTqHLVs2\nMXjwMJo3b0F8fEu6dz8JgMjISIYPP5ennnqUTz+dzc6dySxbtoTCwgLmzPmU2bNnsWTJQq64YgJH\njhxmzZpV/PXXBlJT97J48QIOHEhj/frfufjiMVgsFmbOfJtZs94jJSWJ8PBwfvhhOdnZ2bz11lsU\nFZV/eh89euz/s/feAZJd5YHv79xQsatzT87hzoxGGo2ykJCEBAIBK4JhMWbNYvCu1/jZz/vsXd7C\nc2CNHzZedh/O2MYm2XjBZJAACQySEIoTNNJIoztRkzqn6so3nPfHvVXdPV3dXT3TPV1TdX6aUlfd\nunXr1Fenzne+73zn+8hms3zqU5/kc5/7DHrE4MzPjlUK1Tz++GPTXPktLS3ccsur2Lp1a+XYe9/7\nfk6dOsXnP//3/P3f/w25XA4hBN/5zjfJZrN85StfAuCRR/51WjT8VH7600f42c8e48yZV/jSl75Y\nOe66Ln19fZUCOArFpTJrhjpRztB42Zu0ZFT2uauAuqahIaasS1k45hPP/Cl9uUH+v7v+cMGvvVIL\nxwD83s/+CF9K/vD2j8x4znEcTNPk05/+C/79v/8AiUSi5vf3fZ+PfvT/4Z3v/Hn27Nlb8+s+97nP\nsH37Dm6//Y6aX3MpqOIVtXMlyOoTz51EA/7rtZsrx75+7Lv86PSjfOjG32Bj6/rK8d5ckT8/fJpX\nrWjj/o0rFq0Nyymnl0eO8WcH/5Y3bb6XN2++d1naUCtXQn+qF+YqHNMUbvlLwZP+Re1xh+mFYxZ7\nO9xSowkN13eqPveP//g58vkcK1euXpBiB9A0jQ9+8DdYvXr+pYIyvu9z1133sHnzlvlPVihqZRbL\nvAG3uat97k2IUu7zIKVEu4TViyuxcAwE+/p9Waz63Pvf/x8v6doLUewQTAiUYldcMhfuc5+vKlyV\n2A8pJW5xmFKuFxkGp0npIX0nuHklfN+BKjkiiqMpHL8NM9qJ9B08N4vvF5G+B9IDoSM0HSGM4Kbp\nCBE89v0iXimN5+Uxo52YsR6EZuK7eXwvj+fm8b0CQujoZhKhRcLncuhmK4ZUbvlmQyn3efAucs39\nSkcTAp/6ST+raB58r0gpdw7fLSA0M7jpETTNRNPjaEYSIQRSenhOFqSH0AxA4Lk5fDdXUbwg0PRo\noEC16YmoKvvYi+Ok82fx3Syek2UklwNuYGJoH2cnXkQ3UkEIvfTxnAl8L39RnyszfNEiuWQE8Mut\ncbT8cUbPPYxuJBBapCJfTTMRWgRNj4THy/ejNQXDSunje0WkV8C/4CalH16/fIsEkxsnjedkgvYJ\nDdBAaMh8nGzWRWgGUnrBdf3F3SVTua5XDCZSehRNM2emK6wXpERKF98rIX0nkJc2t/pWyn0efOk1\nqXLXVOGYOsf3iuRGXwgGZyOJEDpSukjfC/+6wcAs9PA5D6Q/7a+Ufmhllq1Uie8V8b0iQuhoehSE\nwCulcZ0JpO8CEuk7+F5gLfbqJoiyQogGVqNXDJQsHrqRRNPj4WumDPp+CU2Po5stwYAvXXyvgFsY\nmueTCzQ9tiAl67lvAXzOv/ggZnwFRqSdlaV+fqElDme+xtiUcx0Z5KfQzBSaHsctjQbyETq6HifW\nupVoYi2aEa+0R2jB5EPoJkKYCE3nQldBKikZ7H8FtziKpkXQjGQoLwOEFnwn5e9OeoGsw/tCMzHM\nNoQexS2O4BQGQfpoRhxNj4WTnjjS9/DcLNIvVo65hWHS40dpzZ0j4o0zMfBEzXJD6OhmCk2PBRMm\n3wOhBW2Wctp3uViMzX+KogaUcp+Hi42Wv9LR66xwjGImmeEDjJ17aJneXQsVSwKh+bilDL5XhCne\nnsDq06Yra6EFSkePIcwUvpunlO8P3NKhkoy2bCCaWIceaQ0mBH4J6QVub8/LhdZzATO+At1oCSy8\ncNKhGXE0I4EQZviGPr5XQvRFEHh4bgZ3LDChNwKYOsTX0rXiJvRIO7qRJObH4fA54m0WazYtXgBn\nqjNFwetZhCttnf+UC3Dar+aPn/gEd6zcy9s33hFMzMpLCb6D9IPlhGBZoRQ89oqhN2MCtziC0Ixg\nkuj7SNelPMkyop3hBKN8i1buCz2GEFr4PQbvE1ieOnqkFd1oqXhFJBKkJJWKkB7PBN9pOMEUmrmg\n7bTzowXX1aOwRN6BxSbwMMTCiVU4SeeTs56vlPs8eNLHuIi88lc6QmiViniK+sQtjgLQuurOyvcV\nrNVOrtkCSD+0/oQeuPNm/NUQiLBymha6KCOh9VhESolhtqJHWhGaSdkiLQ+25ehmKWVloKxYpJRd\ntqGLXRgzBulgbVuGrtmlQRs8ia4J1l3zITxnAq80zqOv/IhHB1/ig9vfTLJlMn+CViiF7Vqy5lx2\nygF1eSDasn7uk5eZzp4Unq6i5S8VpdznwQ/Xi5oNZbnXP14pSJKU6rkFveIiXj6CJQAD/YK1QCE0\ndGP2XRWBsl/qtc4gAa0QAiPSihFpZdhoY8SfmQyqEaPlJ0u+qmj5ZqH5/M0LpFnd8uU1d1VHvX5x\nnXSw3q7HlrspVwQzc8v74fHqhWMaiUpueZV+tmloPq21QJpWuYddo5EqYzUaXmkc3Wxb5LXIxqRa\nL65eE27yQCNNbI1yVThluTcNzae1Foh/CUlsrmTKExoVMV+f+GG0uhFpnf9kRVXmrQp3mduzlOiq\nnnvT0Xxaa4EE+9ybL6CunC5XKff6pLzerkfalrklVzLl3PLTh8FGVO6a0NCEhqfquTcNSrnPQ7O6\n5cveChVUV594pTQAhqks91q5cPXCr2Som+W8RtLugCF0Zbk3Ec2ntRZIoNybb02zsuaulHtd4jpl\ny10p91qotnw+W1W4su3uN5h21zVDrbk3EUq5z4EfJlZoRstdU5Z7XTNpuSu3/KUy25p7o2FoOq6K\nlm8amk9rLYDyzF5vxjV3FVBX17glZbkvhGo2uD9rPffwNY1luGMIQ1WFayKUcp+DstXazJa7Uu71\niecElrsKqKudGfvcw1S5zRAtD6HlrpR709B8WmsB+GFkqVLuinrDLaWDHO1NmD3xctFoyl3XDOWW\nbyKaT2stgLLbrhmVu4qWr1+klHjOeFWrvZB3eOm53oZKwLJ4VMtpP3MrXDmAttFkaAhdueWbCJVb\nfg585ZZXlnsdEpTYdKomsDm8/xxPP3aKeNJk07buZWhdfVJ1zZ3y5L1RQ+imY2iG2grXRDSf1loA\nZatVZahT1BOVBDZVIuUzE0UABntVVa15mcUyb9Q1d13ouNJrOI+EojrNp7UWgFpzV8q9Hinvca9m\nuedzDgBD/ZnL2qb6R1YJqGue9LMAZlitT2Wpaw6aT2stAOWWV8q9Hinvca9mueezQS3yQaXc56Ws\nvLUZW+Eac819Mr+8Uu7NQPNprQXQzAF1KolN/TK5DW52yz07USSfK13WdtUzMijnfsGxWevCNSSG\nKJd9Vcq9GWg+rbUAlFt+ci+won4oJ7Cp7pafVOjKNT83zbfPvVz2VQXVNQPNp7UWQDMH1FW2wvlK\nudcbgVteoJupacdd16NU9CoZ1q405T4+mufBfznE+Gh+Sa4/Y81dlo83R4Y65ZZvLppPay2AZl5z\nF2rNvW5xnXF0swVxQVrkfDZwya9cG1j0g31XVsT8vsdP8crxEV48eP6yvF+zBdQZIrTcVSKbpqD5\ntNYCaGblXrbcfeWWrzt8N49mJGccL7vkV6xuJRozrijLPZctcfSlAQDOnhxd9OtXWXKftSqcaFD1\nboSWu0pk0xw0n9ZaAGXFpgrHKOoFKSXSd6qmnS0H0yWSEbpXtjA+mqdUXH4rTUpJ79lxPG/2vvTS\nc734nkTTBEMDGXLZpQ8GrFjuFz4RHvAbS7dPrrkry70pUMp9DsqK7UK3XTOglHudIn1AIqop91Ah\nxhMm3StbABgaWH7r/fiRQb75jwc4+OTpqs97ns/hA+cwIzrX3boBgDMnR5a8XRXLvUlKvpaNFLXm\n3hwo5T4H5WCyZgyoU8q9PpF+YJ0LbWbm6LLlHk9E6F4ZBNsN9S2/cn9+3zkAjjzfV3Xv+KmjQ2Qn\nSuy4ehVbd/YAl0m5M5tbvvx8YzHplleWezPQfFprATTzmrva516f+DJU7mIOyz1p0hNa7oP9yxtU\nNzyQoe9ssHUvPVag/3x6xjkvhMr/6hvW0tmTJNES4czJ0UVNIlN1zT38O8NyLyexWbR3rw8mt8Ip\ny70ZaD6ttQAmlXvzrbnrynKvSyYt95nKPZcru+UjtHUmiCVMXjk2jFNaPkvthf2B4t59/RoA7Bf6\npz2fHstz/sw4aza009GVQAjB+k0dFHLOkgcEyvKyWxVHvKABM9SV3fJqzb0pUMp9DiYD6ppPTBpK\nudcjcyn38la4eNJE0wTXXL+WYsHlpef6LmsbJ8YLeJ5PseBiH+6npTXK7fdsI540OfbSwLTAuqMv\nBhHy1u6VlWPrt3QCi+yal0xuYL+AajE1jbjuXrbcVbR8c7DsJV8ty3oSKGetcG3bvteyrE7gj4AT\nwHbgI7ZtD1zutqmAOqXc642519xLRKI6hhFYaLuvX8OBJ0/z3DNn2H39GnR96SepR57v48cPHCES\n1enoSuI6PrtvW4NuaGy/aiWHnjnL6eMjbLa6kVJy9HA/ui7YsqOnco11mzoAOH18hOtftXHJ2upL\nWdVqB0A0XhIbo5LERlnuzUA9mKTfs2377vB2b3js48DDtm1/Avgm8MnlaNhkhrrmc8sr5V6fyHBg\nns1yjycilcfxRISde1aTSRc5fmRwydvWe3acR77/MpGoTiRq0H8+jW5o7NyzGpi0zu3DgSdhqD/D\n6HCOjdu6icYmJyvxRIQ169voPTvOyFB22nvkMkX2P/EK3/va8xx7aaDiOpdSzrntT85SFW62ibtA\nNNyau15JYqMs92Zg2S134BrLsj4ExIFnbNt+EHgT8LHw+Z8Bn1+OhqmAOhVQV2+ULfcL97lLKcnn\nSrR2TM83f+3N6zh84BwHnzrN9qtWLJkXKj2W5/tffwHpS97w9t2s3dgRKHddI5EMJhzdK1vo7Ely\n4uUhXth3jvRY4LCzdq+Ycb09N63j/Jlxnnv6DHe/aSdSSn768FFePNiLH25AP3V0mP0/S9LaEef8\n6TGcksdb/91eVq2dWS2vOrNb7oLJaPpGQVnuzUU9aK1P2Lb9JwTK/COWZd0JrADKYb5poMOyrMve\nVt9XhWOkUu51xWxr7sWCi5RMs9wBWtvjbN3Zw/BAlueePrskbfI9n4e++SKFnMOr793Ouk2dCCFY\ntbaNnlWT+e+FENz71quIJ00ee/gohw+cJxoz2LCla8Y1N27rpq0jztHD/eSyJV7Yf44X9p8n1Rbj\njnu3885fugFr90qGB7OctIcwTA3fl+z/2eRe+kLe4aQ9OGtgnJQzrfnJtl6SSOqSySQ2ynJvBpbd\ncrdt+5nwr29Z1mPAa4ABoJVAsbcCo7ZtX3YtowrHKMu93vBnUe65KQlsLuRVd2+l98w4T/z4OMlU\nhM1WN0cO9THUn2Hz9m7Wb+lE0y5em/30X48x2DeBtXslV1+/ds5zO7uTvPU9e/n2Pz9HLlPCunol\nujHz96Vpgj03ruOxh4/y+A+PcdIeJBY3eOt79pJMRQF47f27uOWuzfi+pLU9zje+uJ9Xjg8zPJih\noyvJ9772An1nx7n9dduqRsj5c7jloQHX3IVKP9tMLKtytyxrB3C7bdv/EB7aDnwDeAB4FfAvwO3A\nd+e7Vk9Par5TFkxLJhhE2loTS3L95aKWz9JRCnKXJ5JmQ332hVCXn7ugMwK0taXonNK+7HgRgO6e\n1Ix29/Sk+MVffRWf+4vH+dcHjhD/cYTsRHD+S8/10toe46pr17DF6qG9M8HJo0OcOjZEsRBMJFJt\nce5+4w5a2+IzmtN3fpxHH7ZJtcZ467v3zvAcVKOnJ8UHfiPFk4+c4PZ7ttHWMfO6ALffvY1nfnqK\nY2HO+Xe++zo2bemeca0yd71+B1/+7DMcea6PlWtaK/vrn3nsJPLO1ZiGPu18w9AQQlT9njUh0A1t\n0fvAcvapLjd472hcr8++PYV6b9+VwHJb7mngzZZlrSGw0E/btv1PlmU9CHzCsiwL2Ar8l/kuNDi4\n+Mk6xtJBME82U1qS6y8HPT2pmj7LRDoY/NMT+Yb57AuhVjldbtLpYO/3RMbFm9K+3vOBIpPIqu3W\nDMF9P3c13/3yc5SKLtfduoENWzo5+mI/R18c4MlHTvDkIydmfd8jz/dy5xsstu2aXB8fHsjwo++8\nhO9J7rxvO5lskUy2WPNnuenOTZRcd04579q7mgNPnGb3dWvoXJmc89yOFQk6uhO8sP8chw+eJ5Yw\n2Xvzep78yQk818d1vWmvdxwXENWvKcFx/UXtA8vdpzLpwLsznsnVZd8us9xyahSWVbnbtt0LvKPK\n8VHgVy5/i6ajAupUtHy9Mdua+9TsdLOxZkM77/nVW4lEgmj28rHbX7eNvrPjnDk5SiZdZM2GNtZv\n7gzc3zJQ7D/70XEe/taL/OxHx2jriFMsuAwPBpPf62/dUHXdfDG48baNdHYn2Tplq9xsCCHYe/N6\nfvzgy+BLXnf/LjZb3Zw+PsxZKSkWpgeSSUCbLaBONF4Sm8kMdSqgrhlYbsu9rvGUclfKvc6YVblP\nySs/Fy3hevVUDENn3aZO1m3qrPqa3detZe3GDp748XGG+jOcPzOOpgk2b+/GunolN922meHhpckm\nZ5j6tAQ387F990peOtRL94qWyt75O++zeProWfJ5Z9q5gfJuwMi5WVAlX5sLpdznwG/igDq1Fa4+\nmW0rXLmW+1yW+6XQ3pngje+4BgDX9ZC+xIwYYVvqR0Hqusbbf/H6acc6upJwVOB705Xa3PvcodF6\nvoqWby4aQrk//dOTDA9m8DyfTLrIyFCW7ESR9q4EPStTlQGv4mWTEinDwhAy3M0qw32tleMwkPZo\nLa6mGVMxq61w9clkEpvpP93JaPn5A9oulXIGvCsKEWzZm4qUck63fINtc59S8rUJB7QmpCGU+/e/\n8cK0x4ahEU9G6Ds7Tu+Z8Uu69gau46lTQ5zbdIi29jjJVBTD1CozfiFEMBCIIKtVxRAIjwuCPcgj\nQ1nGRvKYpk48YWJG9SALlpTkcw65TJFozGTLzh42b++qWEXLhdoKV5/MthUun3PQNDEt05tiCgKk\nD6WiW4k3kMhZvfJBhrrG0u7KLd9cNMRI8L5fu42RkSy6Lki0REm1xdA0gVPyGB7MUCy4odINFW54\nHwgfh7/wUBmXFfMT557l0JETbMrt4vTxpa8vDXDyaJCQ483v2sOa9e2X5T2rodbc6xM5S8nXfLZE\nPGE2ZR2EWihLJTNRpLOs3OfKLU/DGe5T3PLKcm8GGkK5b9zaRaJ1pjvSjOgLSEU5k0jJZyBzlHfv\nfQ0bYhvJpItkJor4nh+49St5rZnp6g9HBiklZiQootHeFcdzJYV8iVIxmD0LAbFEhHjCJD2W58UD\nvRx69iwnXx5Syl0xg2oBdVJKctkS7R2J5WrWFUCgxLMTRTq7gxwOQaG46spda8DCMZNueWW5NwMN\nodyXikpAnWYQT0SIJyLT0mleDIbBrK7Tjq4kN9+1mef3nWWgN31J73OpVJR7w4UVXdlUU+4jg1lc\nx6drRXK5mlX/hDo8k57chy/x57TcG41KyVdluTcFzRcGvgCWY5+7aep09bQw1J+ZVvf6cqPW3OsT\n6Tsg9GkW5/nTY0CwZ10xN5mJKcp9Drd8I1aFM5Tl3lQo5T4Hk8r98s7uV6xJ4bo+oxeUu7yciLLl\n7ivlXk9I352xDe6cUu41k52q3JndLS9E41WF01VVuKZCKfc5WK4MdStWB2U7B3qXLwWjrtzydYn0\nnRnr7b1nxki1Rmltr56jXVHe2iYXYLk33pq7JjQ0oSnLvUlQyn0OJqvCXd59vStWB+v6/eeXb91d\nBdTVJxcq95HBLIW8q6z2eZCA0ASZdGHa0bmS2DQihtDVmnuToJT7HCyX5d7RncAwtWW13K9U5Z7L\nlnCcxrVMfOlO2wanXPK1o+vaNLe8P0c9dxowWh6CoDpluTcHKlp+DpZLuWuaRs/KFH3nxnFK7rIk\ntKk35X7m5AhnTo6QSRfJZUtIP9huGInqtLTGMEyN86+MMTyYpa0jzv3vvpZUW2y5m73oBJb7WqEZ\nWQAAIABJREFUZH9QwXQ1IkHXBaWiV0lkI5GIWeybRkxiA8G6u9rn3hwo5T4HFeW+DA6OFWtS9J4d\nZ7AvsywDdz0p90PPnuXxHx6bdqycz9z3Jwdg3dDoWZVisG+Cb/7TAd7yC9fS1kB7v6X0QXoVt7yU\nkvOn1Xp7reh60KezE8VKlrq53PKNqNwNYagMdU2CUu5zsJwlXyeD6tLLotx1oaG5Bu6ISSHvEIsH\nCqVUdAPXdymwgHRDIxIxEJrAKbk4JQ8hBLqhIaWkVPRwSkHdbE0XaJpA1wWapqFpAhEqac/z8T0Z\nZAzUBJoInjt1dIj9T5wm0RLhnjfvpKM7SSIZqSh3p+QykS5SKrh0r2zBMHX2P/EKTz1ykq9/4QBb\ndnSzcm0b7Z1xEskIhqlTzDvkc84FOwGmD/KZsSLj47mqsqmqEETVu+V0iBccqy7zatedekj6Qf54\nxxEM9k2QHitQLLhs2rY05VYbCcmkcs9MFOnoTiLl7PvcGzXRn6HplLzScjdDcRlQyn0OvLB6kq4t\nh3IPgurOnR5j42IN3hLcokd/XxrP8zHNoK63pk9awaWiRz5b4uhLfex8+bUgdT77xOPEEyae51cy\n611OWttj3P/ua6tap2bEoLN7eje+/lUbiUQNnvjxcV482MuLB3svV1OXlEikxL13w9lXJjjw7X2V\n48olXxu6PpmlDsCfI6AOwG88wx1dM3Dd/HI3Q3EZUMp9DpbTck+1xYgnTE4fH7lsee0vxInlMVe4\nbDa3MDqcI25GaElFSbREiEQNzIiOHyp835dEIjpGRAdJJQFPJGpgmsFuA9/38X0Z3DyJL4O/ALoh\n0DUNCUg/eE76Et3QuPq6NSRaZtYhn4urr1/LrmtXMzyQof98monxIoVciVLJI54wiSVMjNCSmzaG\nhw8SySjZbBh8NSWyqtq50w/Jqs9Xf92U686iSOSUc3UtqJne1pHi2pvWAUGK5W27VlR/sWIaWtly\nL2epk8yZxKYRMYWu9rk3CUq5z4EfjqzLodyFENx13w5Onxhe1OumWmO4no9haBXXenndWmiCSNQg\nGjVYsaaFjx/9Y3Z0buNN171tUdtwudB1jRWrWytLHAuhpyfF4ODy7VaohpMfpPcIrFzbya7125a7\nOVccU93yUK4K11xr7rqm1tybBaXc56CSW34ZlDvAZqubzVb3ol6zVqUlpYRj9RFQpwjwZ6kIp5gf\nSeAdkky65eer596IW+F0oeNKL0jg06iBBQpA7XOfEz9cc59tu0wjI4RAIJRyryOqFY1R1I4mBJGo\nPt1yn4XAcm88zErxGGW9NzrNp7UWgLfMlvtyowtNKfc6YlK5K4fbRSEEyVS0suYu58lQ14jKfTK/\nvFLujU5zaq0aWc6AunpACE1VhasjZBgIpSz3i6clFaVUdHFKLlIyq1s+rBzTcJTLvqpENo2PMgHm\noNmVu7Lc64uy5X5hVTjF3Mhw8VwALa1B1sKvfn4/PbqFsdLBdTwMc3r9iEYNqCuXfVVBdY2PUu5z\n4EsfTWhNG3iiKeVeV6g190tn93VrmBgv0HdunDZnHQzAZ488zpr17URiBoau4Xo+6U4d1xQ8+NXn\nMQwNw9TDvxqGoaMbWpCUSYhKMiZNE4iqj4OdKGNDOSYmCpVzEGFsywV/Y3GTltbokow7yi3fPCjl\nPge+9Gd32zUBSrnXF0q5XxxT7e+eVSnuf/e1+L7PRx74H6zKbqZtfCOnT0zPJVG6sQdpmLxybHG3\notaKGdHp6E7Q2Z2kszvJZqt7UVIMG0K55ZsFpdznwAst96VEeh5oc3sHCqdOMbHvGdpuv4PIqlXV\nr+O6DD/wHSaeepKed72blr3XzXo9v1jEGR4iumbtnG1Tyr2+8MvKXaif7aWiaRr51DjO6gHuG+1g\n+LHHiF1zHfHrbyLW083nTw/QXyjxgf/8ajzXw3V9XMfHdT0cx6M4NIpIJEHT8X2JlEFyJhkmaZJS\n4oynKfb3Y6xYhRaPE49HyGSKleeRwZJBcAvv+5DNFBkZyjLUl2HgfLBtdf8Tp3nXB24kmVpYMqcL\nMTTllm8W1CgxB4FbXkdKSeHkSSaefJzMwYOY3d203nobLTfeiJ5IVs6Xvk/x7BlyLzxP/thRImvW\nkrrpZqIbNiKLRbxcFqO1DWEYuOPjDH3jq6Qf/ynR9Rtov+e1pG66BS0a/Hh9x6Fw/BijP3yI7MED\nAIz98CG6f+6dtL/2XkSYElf6PoXjx+j/py9SOnsGgPN/+Wf0vPs9dLz23krbvEyG/PFjjD6/n6En\nnkIWi7TdfQ8rfv49CKN6N9AQweSjTnHHxxCGiZ5MznqOXywy+vAPiKxeTeqGm+a8nnRd0PW6XIbx\nSyVK/eeB5rDc/WIRhECLRBbtmhd+q1JKunozDH37SyAl+fOnyf/gWySv2QN33I8EojGD8jDpl0pk\n9h0k/9APcM+cxuzuoefn301y7/WVPiNdl4mnn2L8sUfIH7WJAyIao/utb2frvffTe+Qkpb4+nL5e\nSn19SKeEnkphtHeQvOZaouvXV9rneT5jA2le3n+a554f4Xv/8Ai3xU4Q37KZxFW7iaxZW3lfv1hk\n4tmncUdHg89qmsQ2biK2eUtlTIEwoE6C4zp4rl/JBFmeYNSKXGASgIWcnokFlR9ne9Gsl5oty+M8\njZISiqdP4Y6MEtu0EaOjc67Ta2ahMpr9Ohf3uvobxS4CKaW8MDGLX8iTPXyY3OHnQddJbN9B3NqB\n0d5+4WvJvXiY0e9/D2EaJK+5luSeazG7uvh/n/pfeMMjfOBIG7kXDwOgJRL4ubCgiBBE160ntnkz\nzsgIhRPHJ5+biq5DqCSFYRBZtx6nvw8/n8fo7Ax+kDLIlmV0dmK0tlE8ewbpBJZabOs2Wq6/gdHv\nPYiXmcDo6CSyejVaIkH+5ZfxJtIAtN15F6mbbqH37z6Nl04TWbceYRj4+TxOf1+lOWZPD0I3KPX1\nEtuylfbX3IOXyeBlJvAyE7gTEziDg+R6zwLQ8/o30Xnfm6oqUd8p4aUnELpeVbZOfz+Fkyco9ffh\nDA4gTJPUTbeQ2HVVZYJyIW46jV8sYKRSiGhshrIt9fUy8uADpJ96Ai0eZ+V7f4nUDTfOuE7+2FH6\nPvsZnP5+ADrufQPd73wXQp8MnvKLRTIH9zPxzNPkXnie+M5drP6VX2XVxlVVk/24Y6PkXnyRyJq1\nRDdunHUi4BcKuGOj+MUi0nXRk0nMlaumne+OjTGx/1kKx44S37GL1ltfVRmIpe9X5JM/foy+z34G\nuaWEcV07yfz1dN7yxmmfQ3oe+aM2mYMHKPX10XrLraRuvmXGOc7QILnDL5A5eAB3fJzO+95I6tbb\nqn4O6ft4E2mk52N2Th/wyhPe7MH9tHSkKMVSmN3dGF3dGO3tVb9bZ3CQ3Esvgq6RuvHmaUqnjJfL\nMfbDhxh9+AcI02TFe36RlhtumlSeUlI4doyJfc/gDA3iDg8RWb2GzjffT3TtumltL5w4Tv74Mdx8\njk+t3cMGv8iv7N1eed//8v0P8d7vjRHPOaz9zd/CS6cZe/QnFI4d5YG3vo+hFWv44BMPoMViOP19\nFM+fC37HQhDfsZP8URs8j+imzUTXrUdPJkg/+STe+FjlnIS1g9EfPYyfzVbtJxcSWbeeyKrVuCND\nOENDeOk0Eji06h6GWjawdXgfm0afB0BrbSWy82pESzuZZ56AzARCemihOpOAr5u467aTW7GVbKKH\nUxN58hkdzVd2XSPw+//rLbPq8IZQ7i/8/h/I/NAwXj6PLJaQrhPM/P2ZLuXErt203XEnemsrpfPn\nmNi/j/yRl2acF1m7jhdTOTYcHyXiSBK7r6b9tfeS3H11MCg/9QSZQ89RPHUysPgAc8VK4tu2kdh9\nDfHtFoVTJ8k8+zSlgQH0lhR6Ik6pr4/iubNo0Rjdb3s7bXfdjTs2xvijPyF/1MYZHMAdGyOydh2J\nHTtp2Xsd8Z27EELgptMMfuWfyb30UjCAAHpbG8nd19B62+0kdu4CwBkapPdv/5ri2UA5CyOcxW/d\nytpX30Khaw2yVKL/C59l4qknq8pURKMMt0A075LMeWiJBLEt29DjMaTv4wwO4gwO4Ocni1BE1q4j\nuftqfKeE09dH4cxp/Eym6vX1tnYSO3cR37oVLR6nePYMxTNnKJ49g5dOT7YjEiG6bh3RDZuQrkPh\n+HFKvYEFa65ciTsygnQcUrfcSmzjJjAMnIF+CsePUzh1EoC219xD/qUXKfX1Et9ukdyzF6Ork/yR\nI0w8/SR+oRC0KdWKN5HGXLWK3R/+EKPjBdzhIZzhYKAtnDgeDOjhVDq6fgPxHTso9fXj9PdW+oFf\nLFad5BmdnSR27MIvFCgN9FM6f27atFyLx4msXYczOIA3Po7e1obZ1UXhZPA54m+zkGsciv98Bs1L\n0rJ3L7FNm8m9fITsoUP4uekKxOzuIbpxI146jTs+jjM8VJlkBh84mHTGt1ukbr0NvSWJLDnk7CNh\nXxysnG90dZGwdiKiUfxigeLJk5T6qhfkEYYRyHnvdRitbeReepHcS4eD65U/ayJB2+13ENu6DbO7\nG2dkhOzBA2QO7MPP59FaWpDFItJxSFy9h9iGDaDrZA89R/GVU9PeS7pusIf96mvQ4gn8UpHC8WN4\nE8HkzBeCL/zKR1h97iRv/NE3SO69Dj2e4KVDj7B6yKHrbT9H1795S+Wa+aM2nzk9Sl9LO+/7uz8K\n3sc0iW7YSHy7Rftr7sbs7qHUe56BL//vwIAop6qOxWi74y7aX/s6zO4eALyJCYa++XXkYB909hBZ\ntYrIqtVEVq1Gi0XxJiYo9fWRfvpJsoeeC2Su65hd3Zhd3RjdXciuVTx0rIVcwUfXAN/Hk6Jq+lxB\ncLha4RshfRKlcSJeAYEfBPL5HloshpFMIF0X6XnBX8cJ+/TkhYz2DvxCAb8we/EZAegtLYhosDNB\nlkp4uWzwuab0d2EYmKtWoUWiIGVgWIyOVn5Hlb5iGmiJZDBRFeCOjoHvIQwz2NFQPl/TMLu70WIx\nhKZTGhiY8puY8r6aBrqBdEqBAtR1zI5OtEQCd2ICLz0ObjDmRdauBV8iS0WckRH87JTxLPz9CMMI\nvstEEBNRPHMaP18I226CpiGLRRCB/LRkEs2M4I6N4o6NQXnpU9MCnSUERmsbeksLaIJSby/SdabI\nI4KIxdCiUd7/x+9rbOX+xM//OykBLRZHRCOBqzYeJ75jJ8k9e0H65G2b7KGDweB8Aclr9tD19neg\nxxNkn3+OzKFD5I+8iHRdSqbG+l98P623vbq6deO6lHrPo7e1Y7TWlsO8PBhNtaqmPT/FapsNv1DA\nm5jA6Oqa99ypTE0/K6Uk+9xBvHQaPdUSTEBagr9aSwsfe+qTFPNZfrt0S8VrUEaYJmbPCoz2dvRU\nCi+bJX/kpWk/TLO7h9iWrcS2bCW6di3mihW4I6Okn3yciX3PVlX8Rnd3YAXFgx+aOzYaKPOy5yMa\nI751K2133U3LdddT6uuj7zN/Q/H0K9MvpOvENm2m553vIr7dwsvl6Pv7vyX73MHp79fZSettryZ1\n8y1EVq1m6GtfYfQH368uPCGIb9tOcs9eCieOkzl0sNIuvbUVLRzMhGkGHpj2jmCgMQzc4SGyLx6u\nWHBaLEZ0/QZabryJhLWDif37GH/0J3jpdDCgd3Tgjo3iDA1hrljByvd9gHz0ZbIjB4nYa8k8cWCa\n/IzOTpLXXkfL3uswu7oZ/dFDpB97tNLX9JYWzJ4VmD0riG/dSnLvdeD7DHz5n8ke2D/jo2rxOJHV\nqzE6OpG+T95+eZr1KQyDluuuJ3XLq2jvSjF04gzO0BDu8BCl3l6KZ07PuF5i51Ukdu3CnZhg/Cc/\nrnicpn1t7e10vPb1tN99D+74GP2f/yx5++Vp30HL3utpu/seYhs2oiWTZJ9/juFvf4tiOJmDYKKW\nvHYvyauvRqTa+HhaY30xw+u/+Xm8sbHKeYPrWrnt9z414zf0Ny+d4XSmwMeuWY+XzwdepFmWr3yn\nhDs0hDM6SmzTZvREoup5taR+9nJZ/EKxqvej/3yaJ398HMcJlIFhaBjSQfMcRLIFXwo8zw/c7b5E\n1zV0Q6MlFaHdLNFaGGTo7LNkzh1n7bZr2fLGd6DFE8Hy4E8fm/ZeIhpDTyZJ7LqKtjvvAmDgS/8Y\nTKx0ndQNN9Fyw41o8TiaaaKnWjHa2yicOsXI9x4gd/iFadczV6wktnkzsU3BLX/sKCPfe2DGJFhv\na6fntlspaSZISWlggMKJY7gjI9PO6XrLW2m7/Y7AM1nIM/7Yo4w+/INp5yEEqZtvpeMN94EvccdG\nyR+1yb7wPO7YKMk915K68WYSV+0OlHD5O8hkGPr6vzD+6CPTvxxdJ3nVblquu4HErqvQ29sZ+9cf\nMvLdb08zctA02l9zD133vxU9lUL6Ppl9zzL8nW8FE/opRNatJ3X9DbTccCPmipVMPP0UYz98aNrv\nR0sm6Xj9fZgdnWQPB0u+7ugo+D6v/vbXG1u5V3PLz0ap9zzpJ59A+j7RNWuJbthIdO3MwDK/WOSv\nv/uHjLaZ/M7rfm+xm7xsLKQgyh8+9T9Jlyb4kzs+GqzJlUphJ5borW0zBh+/WKRw8kQw4125qqrb\ntYz0fZz+PvLHjyOdEtF164msXVd1YPQdh9L5cwhNJ7J27Yz3la5L/qgdusAdjLZ2ohs3zVivlVJS\n6u3FGejHGRwgsmo1id1Xz7he+smfUTy4Dy+erFhPZnc35spV0yZw7vg4ztAgkZWrgln2PEjfp9TX\nG0yiUqkZk0Xp++D705TI1DiAoZNfIzd2mLVX/xaaFid//BjFM6eJb9tOdMPMJQIvn0c6DnoyOetE\nEiB/4gROfx9eqLyD622YJpeg7X0gJVosGkwAw++3Wp9yRkfJPncAP58nvmMXsY0bpy+HOE5gzff3\n4wwNocVjJPdcR2zTppnve+5s8Flcl8jKVZhdM0sgSylxR0dAaGiGgZZMVq7j+ZLf3XeMra1xPrBt\nNc5AP77j8MdPf4r2TRb/+cYPzrje3x45y6mJPB+/afusclso9VCM6OFXfsI3jz/Ir+75Ja7pvqpy\n3BkeRpaKaMkW9ESi6kQmiO85jrmiB6Nt7jLD7tgYfimoG68nElV/H142S+bAfqTnIoSGuWIFcWsH\nK1a2zZCTl8siS07w+27vqN4+16Vw+hW8dBovkyG2efO0pZqFkj9xgvzRlwODJ9VKfMvW6p8jkyF/\n4liwjDIxQeqGG4muWz/jPCklXjpN6fw5nJER4tu2E1m5sup7e7ksxdOnccfHSe65Fj0+fadEecls\nzfYNs+rwplt4iaxeQ/fb3zHveVo0Su+qeNMmsIHp0fJCCEQ0OqfC1qLRytLAfAhNI7J6DZHVa+Zv\nRxgcNOu1DIPErqtmfb5ynhBE16whumbu92y99TZ67n/DvAOx0daG0dY27/tW3l/T5tyhIDQtcM1N\nPTZV0U9JPyt0nYS1g4S1Y9br6fE4xOffPhXfsoX4li01tH3+76qM2dFB+2vumfV5zTRp2bN33usI\nTSO6fsP85wmB2TlT6c84T9eJrF6DL30Gj5l0zOL1Ko+YjVZgpZKh7oJo+WoTpgsRmkZ8e22TnQvj\nb6qhJ5O0vfqOmq6nJ5JQ3SFSQRgG8S1ba7peLdTyu4BgCaKmvixEzWOGnkjOOZYKTZt3gtW8mqsG\nfOk1bV55CJS7Sj9bP/hqn/tFUV5traai58otP/W1jcLkVji1z73RaV7NVQO+lE1vuUul3OsGKR1A\nQ4jZXeyK2phMSTt7yVdoQOVeSWJTv1tcFYtD82quGvAvQxKbekZXlntdIX1XVYS7KGba7n54bF7L\nvcG0+2T6WWW5NzrNq7lq4HJkqKtnBCpDXT0hfUe55BeL+Sz3BnXMT1aFU5Z7o9O8mqsGfOk1tXIv\nxxsoBV8fKOV+cVRbc69UfJsnVq6xVPtkVThluTc+zau5asCXftMH1IFS7vWC9B1V7nWR8MtJZ+Zb\nc28w7a6HlrvKLd/4NK/mqgEfFVAHSrnXC8pyX0xqXHO/TK25XJjlNXfllm94mldzzYOUsukD6sqf\nXQXVLT9BYQ8VULdYyDk3yM2+Fn+lo5ej5ZVbvuFpXs01D2VrtZmVe3lJQm2HW35kWH9bCGW5L5Sy\na32qkV4+piE4cHSQv/zG8zjuFGu24pZvLNtdlXxtHppXc82DUu7Kcq8npEpgs6hMBtQJfvjsWfa9\nPIh9ZrzyfKO65Sej5ZXl3ug0r+aah7JCUwF1as29HlDKfXGpJLGRcKI3KGBz9OxkQZlGVe56JVpe\nWe6NTvNqrnmQlC335s0GppR7/aCU++IgpWSsOM7x8VMAFEo+xVKg6I6enWK5N2i0vHGFR8sHsSf1\n9aV4vofjOfOfOIWL/Qy+9Mk42ZreryGic752+EGy2RJxI4ZEUnALlHyHqB4hqkcrHbrM1FAZX0oK\nXoGCWwSCNSlDMya3yijLnQMDh2iLtuJLieu7FL0SGSdDxslhCJ2YEUUTGnm3QMEtEDNitEZSAAzm\nhxktjNESSdIT78LQDIbyw4wWxklFWuiJd5E0E7i+i+t7ONIN7wc3X/okzAQpM4mPZKKUYaKUIV2a\nYKKUQdd0umIdtEfbMDQDXWgIoaELDe3CG8FxQzMm1x6lXwme9KWPDB22LeNRJiYKoSQmw68kkuDf\n5I8zOCbD+xc8Jy84D4jqUeJGDNf3GC2OkSllSJpJOqJtxM3qhV700hgdwFBhjFcGDlU9x/EcCl6R\nolesBISVo8E1gtrf5f+CfyKs/S3QhYGpGZS8Emcy5zmX6cXUDNqjbaQiLTPkKMRkyFlyMEouW5r2\nOYteiYJXwPGcIBvcFJlJZCguGd6fcjwQGhLIuXnGCmNknCypSAsd0XbiZrzy3Ra9EkW3SESP0BFr\nozXSSkQ3MYQRvLY4TtbJYmpxYA+n02f5bz/9DBlnsnRtsTA5Gpw4n8bzfXRNozxKTP0uG4Fyv39u\n6AV6n+knopuVvu9LGdTT0Ay6Yh10xTuRUpJzc2SdPDk3T97NB5UBhR72B4EmNBzfpeQFfSBhxkkY\nQZUXX3rhdf2ajYRIxEDzDWJGFIEIx5wiQ/kRhgojICUtkRYSRhxJcG1DMyrjfXCL4EtJyS9R8iZv\nTjiuBO1MkDQTlfdwpYfne7gyGIs830XXdLrjXXTFOnF9hwknQ9ENPqcnfcaK44wWx4JxyojTGkkh\nAdd3AEFEN4noEaJahIgeoegVGcwPM15M0xnrYHVyBQkzQdEtBv3ZK1LwimhCI2kkiBkxPOnh+C7j\nxTTDhZFK+415DM+GUO5ffuE7S3bthDF/Va1GJREqmq8d++4yt6Q6UT2C63u8kj6z3E1ZctbqGr/Y\nmuDg0BEePVtduS8mAlEXii1lttAaSZFxsvTnBi/yKgZtqT1MOBlieoS97ZtZlVjBikQPB/dpwCjb\n1rZx7Nw4ZwYybFrV2rBu+dZIil2dFmcnznM+24vjuwjEtEmx47ucSp+u+vqIZiKEmKawJRJD6ET0\nCBLJ+WzfkrQ9bsRYk1yJJnQypQxjxXGEEGhouNKl4BZn7bMCgambRDQTQzOQUtKb7cOZsmtAExq6\nCIw7Q+jomk7eKczZ71ojKTa1rsfUTMZLE0yUJhCIikE5XsxT8kqVrYcCQXu0jY2t6xnOj/DC8JFp\n19OFHk5MfM55vRd8/jhrkqtoj7bh+E7FIJ2NhlDuH737w7wyMEjBK6AhiBoxTGFQkg4Ft4DvzzFj\nFIJYOOMDwtlbMLvzpGRr2ybGSwtzudQzer5U8+d59Zq7WRHfgCOdwI0nNEyhY+omSTNB0kjgSY+C\nV8SXkrgeI2JEKLpFMk4GX0q6Yh20RVvJOnmGCyN4vktXvJO2SIoJJ8dIYZS8Wwg8JpUfloGu6Rha\nYB3knDxZN4cAWswWWiJJWsxkaHVIMqUM46UJfN/HZ7oVUp7Ze+VjvoeHF7olRTA4hAOEJrSKxduS\nipHJFKd4ecS0aGvBlM1SFSt28v9T909fuK2q6DkU/QICjbZoKykzSdbJMV5MU/RLVCNWGiMz8QLr\nO3Zyf2JmrWgIrLKYHiMSuu4rlnJoCZc9DOX7Fes5HKgd6aILjVXJlaxI9CClJF2aIOfkgnPwkfh4\nF7hGW1Mx0hUvR0BEM4kZgddMoFVkV5aQqHgOyrIR045DMHkz9ckhquQFFlzZGoxoESK6SdErkS6l\nA3dl6AGKG1FaIykSRoKsW+ILx9Ls7NjOL+987bR2fuv8k8SjOndeu4Zj58Y5emZ8mnJPl9yKF+9S\nWchvbyl571Xvq9yXcvouAgi8melSmtHCOLrQiRsx4kY8/D5nWosXXsOXkmKoeDQhEEKvWPi10NGZ\noG9glIJXQBJYqKZmEjNiM9p6YTvKnsWSX0ITAlOLYGompmZUfW3JC61gLWhjNfJOgdHiOKZu0mIk\niBqTZa9ne82FeL6P4zvoQp/Wp7OlPCW/RFSPBl6nKfL1fJ+iV6p4G/Uq5Yn/hA/P+p4Nodw/9ewQ\nEgFMtbJdmHFsLqauQRlURHO+fxFaeCWTnOW4BLIXHCuEN4Dy2nB2ynkaEAEy4a187MJCzW54m0r5\nB+UAY+FtLgSX1L17qdKuxcJger/MhX9b53hNN7BtuuiWjAIwlzdEMH1xq8TM8B2Pyc91udDDW5nc\ntDZMHVQBMnmHvpEcuzd1YG0IamMfPTfOvTetrwzaf/li43uF5sYn+M1NLHdD6oA8kF7uRtRM3Sp3\ny7JeB7wdGACkbdt/MNu5d6zvJp2d20WxWEjpISlbZmKGVVavSHyk7xKNmpScxk3SsVhEYwbFQv1s\nF3JLYxSzZ4gk1mBGu5a7OdOoN1lVRcCN3dMnTyfOBwF0W9e20dMWoy0Z4ejZMaSU3LayHU0sbkDd\nFSGnOkDJaXGoS+VuWVYC+GvgKtu2HcuyvmpZ1j22bf9rtfPf0HqaCZFFaAa60YIZ68GSLs+LAAAM\ntklEQVSIdiB9B98rhLc8vluoPJaV9IsCTY+i6dHAfemXkNJD00yEZuI5EziFYZziEG5hGN/LT3ln\ngdBMhBaZNY1lPSClh++GFkwGQKAZyTnbrOlx9EgrutGCEBqEN4FAaAaakUQ3EghhTPHJBRMe6Rfx\n3DzSL4Zu4MARHIyUZZeuj/Q9pPQQQkMzEuhGEt1IohkJpPRwiyO4pXEIv6vKelp4nan3pz4npQ/S\nRUofoZmV71JoJpoew4h2YEQ6AInn5vC9SZey7xXw3SwR0yFbGsP3cmh6HCPaiWYkg/7hFRF6BN1I\ngtDwSmk8NxOcF2lH02NT+l1wQ0qEHkHTgnVJ6btBUJoWRdMjYV8thslqgvVPTY+hGQlAUJg4Sa5w\niM6Vb6Gla9Ul94nFpKcnxeDglWfZHTsXWGFb17YhhGD7ujaefXmQofEC69vjrG9ZXDlfqXK63Cg5\n1c6vz/FcXSp34FXAK7ZtlxeoHgfeDFRV7mde/tZlaJLAiHYQSa5DCA3pO8GA7DtIv1TXe2aEFsGM\nrcCItBKJCHITI3huds42u6VxnMLAZWylohZ0s2W5m9AwHD8XWO5b1gQW/bZ17Tz78iBHTo/S0968\ngbSKxqBelfsKpi/ypMNjVfnMvjtxHBcB6MLF0Fx0zUNKgZQaPgJfauF9DV9OXzMU+GgitC9l2d0u\nQUiKDmQKUPJ0hBDEIwaadvFW+nIb+JoQ+LKztnPx0UQQjBi0W4bBYhJNBM+JKtGpsiLv0JK/4Lnw\nTmjPBwsEmvDRhY8mvPA9Ba6v40ljxhxEzrGkUK7SHbx30FaBrPzVhI+heegiiMnwpYYvJ9eLg76i\nAxquF/QXTfgYwkUTPj7B5xJCoovAo+BJHV9qaPjoWtD+8nXL/Q8ZtkWUg9zK29D8MC496KtTM54L\nMSl/KYN2lX7aCyxNNPLFomkC31++ye3F/qbGsyViEZ3PPRhELGcLgS3x2QeP8IXvv0zE0OYtCVsm\nCBwL5OCFAbyGrmHoGr6UeFPkUw64DIIHJ+9fLFKC58vgOxCga0GQqLxgy6aYEhS63OPQXGiaNncQ\ntKIm6lW59wOpKY/bwmNV+fRH3lLHXVWhUNQzX13uBigUS0C9Zmh5EthoWVYkfHwb8MAytkehUCgU\niiuGurV4w2j5dwKDQMm27Y8tc5MUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqGoPyzL\nqttgwHpCyak2lJxqR8mqNpScakfJqjYWIqcrRqCWZd0PXAUM27b9meVuT72i5FQbSk61o2RVG0pO\ntaNkVRuXIqe6V+6WZZnAbwHDwA+BrwNfAf7Mtu3LXXaqblFyqg0lp9pRsqoNJafaUbKqjcWQU70m\nsakQ5pdfAxy1bfsU8H8A/4Yg/7wiRMmpNpScakfJqjaUnGpHyao2FkNOdZd+1rIsDXg3Qf2y/QSJ\ntA1gpWVZEdu2n7As61ng3wI/sixLs2276RIRKznVhpJT7ShZ1YaSU+0oWdXGUsiprix3y7I2AX8O\nRIFrgD8FuoBjwBuATeGpfwTcZ1lWW5N2hE0oOc2LklPtKFnVhpJT7ShZ1cZSyamulDvBh3jBtu3P\nAn8JvELwof8K6ATeZllWF0GswBeBZl2j2YSSUy1sQsmpVjahZFULm1ByqpVNKFnVwiaWQE71ptx3\nE7gmICjz+kmCSMFrgN8B4sAfA/8R+NGUeu/NhpJTbSg51Y6SVW0oOdWOklVtNKacLMsypu7dsyxr\nMAz/x7KsmGVZv2NZ1v855fnrl6Ody42SU20oOdWOklVtKDnVjpJVbTSknCzLus2yrI+UP8iU4zeH\nf3/DsqyzU45/1LKs11/udi43Sk61oeRUO0pWtaHkVDtKVrWxHHK6rPvcLcv6r0AR2Ad8GDhu2/Zv\nhrOSvwdut207Z1nW14EjgA+MA1+xbfuVy9nW5UTJqTaUnGpHyao2lJxqR8mqNppCTpZl/YFlWbeF\n9zdaltVrWdZd4ePElPNaLMvaYFnW3cvV1uVEyak2lJxqR8mqNpScakfJqjaWS05Lus89dCu0AA8B\nLrAN6LIsy7Rt+xXLsr4I/C7wCFCyLOvPgH+xbfsxgv1+p5eyffWCZVnvAlYDDxBESm4FOpWcpqP6\nU+2oPlUbqk/VjupTtVEvfWpJouUty+oOG7wBeA3wcWAF8ATwn4BUeOpHgB7Lsrbatu0CBwk6TVNg\nWdYtlmV9CFhJ0AF+K4yEtIH/gJIToPrTQlB9qjZUn6od1adqo976lL7YFwTo6uq6EYjZtv1XXV1d\nLwEWQaj/fwN+LTznKNBGsFn/G8PDw/7w8PCB4eHh8aVoUz3S1dX1B8B+27b/oauraxy4Y3h4+Otd\nXV1PEqQblEpOqj8tBNWnakP1qdpRfao26q1PLZVyvxn4zeHh4U8PDw+PdXV1DQP3E8xOvgvcEj7e\nAhywbfvFpWhHvWJZlj48PCy7urpeAc6H8nkHsKmrq6sTmAAeBF5NkE+4KeVURvWn+VF9amGoPjU/\nqk8tjHrrU4um3C3L0rq6usSUzvBvw8cHurq6XGAtELFt+4Hh4eEfhR/8a7ZtH16sNlwJWEEOYTk8\nPMzw8HD/8PDwUPhDiQPfJvihvBP4tG3bDzWrnCCQlepPtdHV1YXqU3OjxqjaUeNUbViWpTdcn7Is\na5cV7Nu72bKs5JTju8O/v2xZ1iHLsqLh449alvXzy9Xe5WIOOV09y/nvtyzr9y9fC+sDy7JusCyr\ns8px1Z8uYA5ZqT41BcuydluW9d/DNeP41OPhX9WnQuaQlepTU7As6yrLsv7Usqz3WZbVNuV43fWp\nBe1ztyxL2LYtLcv6IMG6wUngLiBh2/YvWZa1kyCS8gbbtscsy/oSgevmOEHw3rebwWWzADndaNv2\nqGVZe4CdgCRIO/iwbds/W672X27CrR/fAf7Etu0/mHJ8O/B9mrw/TWUeWX0PuKnZ+5RlWR3ALxB8\n9hbgauAJ27Y/bVmWRSAn1aeoSVYPovoUlmWlgF8nyOs+CLwJOGLb9h82RJ8qW56WZX3Ysqy94f0u\ny7IylmXdFz7umHK+aVnWDsuy3rQ8LV4eapRT+5TzN1uW9SXrguxFjY4V7vG0LGunZVn/wbKsw+HE\nZ+o5LVPuN2V/gpplNdUz1Kx9SliW9QbLsv58yrHfsSzrF6Y8Vn2KmmWl+lSQKvZay7I+MeXYhy3L\nunfK46njeV30qZosd8uyXgW8C4gRVK35n8Bf2bb9rfD53wZ+ybbta8LHv0MQXfngkrS6TrkIOf0u\nsM+27Qcty4ratl1cpqZfVqbIKU5Q3rBk2/Zxy7L+N8E+zw9OLY7QrP0JLk1WTdin3k0wpv0dsMm2\n7e+Ez/3fwHnbtr845fzKb2852rucXIqsmrRPacDfAGts237IsqxdwGeAR4E227Z/LTy/rvrUnPvc\nLctKWZb1SWAP8DGCPXtvI3ALfiI8RxAosrOh2waC6MD+pWp0vbEYcmqGH0wVOXUCv27b9vHwlA8D\nryOIKsWyrHKSpdM0UX+CxZFVE/ap/06QZOVXysoqZBPwVSvghvDYGZq7Ty1EVmdp7j61CvhPtm0/\nFJ5SBD5AUF/9fitI7gN1pvfmS2LjA+cJysyNEHzQm23b/gvAtSzr/7JtWxLs2TsCHAawbfuLtm3v\nW8J21xsXK6cvNLmcPgZssixLt4I4hZMElsTHLct6L7ALmlJOoGRVK9V+e1tCOZXHt0MEmcI+Avwy\ngG3bn2syOcHFy+qzTSarWeUUPn/Stu2Xw/O+AxyD+vvtzafccwTJ64+FjwXwZHj/d4GdlmX9D4JO\n8Kxt297SNLPuUXKqjQvlpAE/CeWxKjx2kCBYx7Rt+/llaGO9oGRVGxfKSWdSTknLsrYCvw08DBwu\nu1CbFCWr2qgmpx/btu1ZlrUD+FXLst5OkJhmn23b+5eroXMxZ2750No8O+XQRuCl0MUcJUiv10Mw\nkxleslbWOUpOtTGLnI6G9++zLOshAtfWVbZtD1zu9tUTSla1MY+c7gfuIVgb/bBt23XjMl0OlKxq\nYxY5lRX9boK96ybwd7Ztj17m5tXMQgvHrAJGgC8RrDv8wL5SStJdXpScamMVMGJZ1j8DBYItIy8s\nc5vqFSWr2ijL6SvAAPB7tm2fX+Y21StKVrVRltOXgVHg96+EyU/N+9wty1pFkAD/eeDLtm3/05K1\n6gpGyak2lJxqR8mqNpScakfJqjauZDktxHL3CQrL/4lt26Ulak8joORUG0pOtaNkVRtKTrWjZFUb\nSk4KhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqF4v9vDw4IAAAAAIT8f92QAAAA\nAAAAAD8Bf81KL3Xv5tQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f2191c05510>"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Metrics"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```python\n",
      "from nilmtk.metrics import (mean_normalized_error_power, \n",
      "                            fraction_energy_assigned_correctly, \n",
      "                            f_score,\n",
      "                            rms_error_power)\n",
      "```"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "metrics = {\n",
      "    'mean normalized error power':\n",
      "          mean_normalized_error_power,\n",
      "    'fraction energy assigned correctly': \n",
      "          fraction_energy_assigned_correctly,\n",
      "    'f score': f_score,\n",
      "    'RMS Error Power': rms_error_power\n",
      "}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for metric_name, metric_func in metrics.iteritems():\n",
      "    result = metric_func(predicted_power, ground_truth_power)\n",
      "    display(HTML('<h5>' + metric_name + '</h5>'))\n",
      "    if isinstance(result, dict):\n",
      "        pretty_print_dict(result)\n",
      "    else:\n",
      "        print(result)\n",
      "    print()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<h5>RMS Error Power</h5>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x7f2191b6af90>"
       ]
      },
      {
       "html": [
        "<ul><li><strong>ApplianceName(name='fridge', instance=1)</strong>: 110.208348899</li><li><strong>ApplianceName(name='washer dryer', instance=1)</strong>: 216.400642527</li><li><strong>ApplianceName(name='kitchen outlets', instance=2)</strong>: 18.8756186696</li><li><strong>ApplianceName(name='kitchen outlets', instance=1)</strong>: 10.5264942354</li><li><strong>ApplianceName(name='lighting', instance=1)</strong>: 69.2372594931</li><li><strong>ApplianceName(name='dishwasher', instance=1)</strong>: 221.145976367</li><li><strong>ApplianceName(name='microwave', instance=1)</strong>: 223.294611141</li></ul>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x7f2191b6a210>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "html": [
        "<h5>f score</h5>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x7f2191b6a6d0>"
       ]
      },
      {
       "html": [
        "<ul><li><strong>ApplianceName(name='fridge', instance=1)</strong>: 0.486390231493</li><li><strong>ApplianceName(name='washer dryer', instance=1)</strong>: 0.692607003891</li><li><strong>ApplianceName(name='kitchen outlets', instance=2)</strong>: 0.0</li><li><strong>ApplianceName(name='kitchen outlets', instance=1)</strong>: 0.0</li><li><strong>ApplianceName(name='lighting', instance=1)</strong>: 0.517505876531</li><li><strong>ApplianceName(name='dishwasher', instance=1)</strong>: 0.236493374108</li><li><strong>ApplianceName(name='microwave', instance=1)</strong>: 0.25</li></ul>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x7f2191b6a690>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "html": [
        "<h5>fraction energy assigned correctly</h5>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x7f21dc1cc6d0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.771400439401\n",
        "\n"
       ]
      },
      {
       "html": [
        "<h5>mean normalized error power</h5>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x7f2191ca0b50>"
       ]
      },
      {
       "html": [
        "<ul><li><strong>ApplianceName(name='fridge', instance=1)</strong>: 1.60618649799</li><li><strong>ApplianceName(name='washer dryer', instance=1)</strong>: 0.703450115919</li><li><strong>ApplianceName(name='kitchen outlets', instance=2)</strong>: 0.714609954031</li><li><strong>ApplianceName(name='kitchen outlets', instance=1)</strong>: 0.516469554455</li><li><strong>ApplianceName(name='lighting', instance=1)</strong>: 1.36871998661</li><li><strong>ApplianceName(name='dishwasher', instance=1)</strong>: 2.85568760678</li><li><strong>ApplianceName(name='microwave', instance=1)</strong>: 3.57270358602</li></ul>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x7f2191ca00d0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![](static/img/metrics.png)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![](static/img/fscore.png)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "End"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "nilmtk website: nilmtk.github.io\n",
      "\n",
      "Contact: jack.kelly@imperial.ac.uk\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# CSS styling\n",
      "from IPython.core.display import display, HTML\n",
      "display(HTML(open('static/styles.css', 'r').read()));"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<style>\n",
        "\n",
        "@font-face{\n",
        "    font-family:\"AvenirNext-UltraLight\";\n",
        "    src:url(\"styles/fonts/17c55b27-e9ab-43cd-b948-d49f97480d68.eot?#iefix\");\n",
        "    src:url(\"styles/fonts/17c55b27-e9ab-43cd-b948-d49f97480d68.eot?#iefix\") format(\"eot\"),url(\"styles/fonts/fa19948e-5e38-4909-b31e-41acd170d6f2.woff\") format(\"woff\"),url(\"styles/fonts/6de0ce4d-9278-467b-b96f-c1f5f0a4c375.ttf\") format(\"truetype\"),url(\"styles/fonts/9fd4ea0c-b19a-4b21-9fdf-37045707dd78.svg#9fd4ea0c-b19a-4b21-9fdf-37045707dd78\") format(\"svg\");\n",
        "}\n",
        "\n",
        "@font-face{\n",
        "    font-family:\"AvenirNext\";\n",
        "    src:url(\"styles/fonts/e9167238-3b3f-4813-a04a-a384394eed42.eot?#iefix\");\n",
        "    src:url(\"styles/fonts/e9167238-3b3f-4813-a04a-a384394eed42.eot?#iefix\") format(\"eot\"),url(\"styles/fonts/1e9892c0-6927-4412-9874-1b82801ba47a.woff\") format(\"woff\"),url(\"styles/fonts/46cf1067-688d-4aab-b0f7-bd942af6efd8.ttf\") format(\"truetype\"),url(\"styles/fonts/52a192b1-bea5-4b48-879f-107f009b666f.svg#52a192b1-bea5-4b48-879f-107f009b666f\") format(\"svg\");\n",
        "}\n",
        "\n",
        "body, p {\n",
        "  color: #2d3c48;\n",
        "  font-family: 'AvenirNext', sans-serif;\n",
        "  font-weight: normal;\n",
        "  -webkit-font-smoothing: antialiased;\n",
        "}\n",
        "\n",
        ".thumbnail {\n",
        "\tfloat: left;\n",
        "\twidth: 100%;\n",
        "\theight: 100%;\n",
        "\tmargin-right: 10px;\n",
        "\tmargin-bottom: 10px;\n",
        "\t-web-kit-shadow: None !important;\n",
        "\tbox-shadow: None !important;\n",
        "}\n",
        "\n",
        ".test {\n",
        "    float: left;\n",
        "    position: relative;\n",
        "    width:28%;\n",
        "    height:100%;\n",
        "    margin-right: 20px;\n",
        "}\n",
        ".test img {\n",
        "    float: left;\n",
        "    width:100%;\n",
        "    height:100%;\n",
        "}\n",
        ".test:after {\n",
        "    font-size: 40px;\n",
        "    text-align: center;\n",
        "    content: attr(data-content);\n",
        "    color:#fff;\n",
        "    position: absolute;\n",
        "    width:100%; height:66%;\n",
        "    top:0px; left:0px;\n",
        "    background:rgba(0,0,0,0.6);\n",
        "    opacity:0;\n",
        "    transition: all 0.5s;\n",
        "    -webkit-transition: all 0.5s;\n",
        "    padding-left: 5px;\n",
        "    padding-right: 5px;\n",
        "    padding-top: 20%;\n",
        "    border-radius: 4px;\n",
        "    margin-right: 10px;\n",
        "}\n",
        ".test:hover:after {\n",
        "    opacity:0.8;\n",
        "}\n",
        "\n",
        "h1,\n",
        "h2,\n",
        "h3,\n",
        "h4,\n",
        "h5,\n",
        "h6 {\n",
        "    font-family: 'AvenirNext-UltraLight', sans-serif;\n",
        "    font-weight: bold;\n",
        "    color: #545E6B;\n",
        "}\n",
        "\n",
        "p {\n",
        "    color: #222;\n",
        "}\n",
        "\n",
        "a {\n",
        "    text-decoration: none !important;\n",
        "    color: #8F7BD9;\n",
        "    line-height: 19px;\n",
        "    transition: all .25s; \n",
        "    -moz-transition: all .25s; \n",
        "    -webkit-transition: all .25s;\n",
        "}\n",
        "\n",
        "a:hover {\n",
        "\tcolor: #A997EB;\n",
        "\ttext-decoration: none !important;\n",
        "}\n",
        "\n",
        "a:active {\n",
        "\tcolor: #59A2E6;\n",
        "\ttext-decoration: none !important;\n",
        "}\n",
        "\n",
        ".button {\n",
        "  padding-left: 40px;\n",
        "  padding-right: 40px;\n",
        "  padding-top: 10px;\n",
        "  padding-bottom: 10px;\n",
        "  margin-right: 20px;\n",
        "  border: 1px solid #9175f0;\n",
        "  box-shadow: inset 0px 1px 0px 0px rgba(255,255,255,0.30);\n",
        "  border-radius: 3px;\n",
        "  font-size: 24px;\n",
        "  color: #9175f0;\n",
        "  line-height: 26px;\n",
        "}\n",
        "\n",
        ".button:hover {\n",
        "  color: #A997EB;\n",
        "  background-color: rgb(240,240,240);\n",
        "\n",
        "}\n"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10449abd0>"
       ]
      }
     ],
     "prompt_number": 1
    }
   ],
   "metadata": {}
  }
 ]
}